[User] Please transcribe this text

[Assistant] Don't change the subject. Are you going to ask her to get out or am I?


[User] Please transcribe this text

[Assistant] I was just wondering if my wedding gown could be altered to fit you. I think it could. Well, that is, of course, if you like blue. Oh, it would go wonderfully with your hair and eyes. And I'd be more than happy if you'd wear it. I could bring it over and you could try it on, huh? Oh, but you'll have some ideas of your own. You'd look lovely in white, too.


[User] Please transcribe this text

[Assistant] Why? Oh, look Sam, now it can be told. I'm sick of looking at it. I'm tired of every piece. The second-hand dealers would give us little or nothing, so I call the Salvation Army. Why not give it to them? I felt that's what you'd want me to do.


[User] Please transcribe this text

[Assistant] It'll help brighten some poor family's Christmas. Help make it as merry as ours is going to be. What's the matter, darling? What's wrong? Oh, nothing. What made you say that? You've been working too hard. That annual Christmas dinner must be an awful headache.


[User] Please transcribe this text

[Assistant] We're going to have dinner tonight in our own new home. Chloe's out buying everything. And it will consist of all your favorite dishes, including me.


[User] Please transcribe this text

[Assistant] Yes, according to the banker man here, he's going to loan us the money so the house will be ours too. That's right. And he hasn't been drinking.


[User] Please transcribe this text

[Assistant] Hello and welcome to the Ultimate Sports Quiz 2022, India's biggest celebration of sporting knowledge and excellence. Let's call the teams into our sporting arena. Our first team in today's contest is from a state that's setting


[User] Please transcribe this text

[Assistant] Wonderful benchmarks in the promotion of sport. The area that's synonymous with the team from an erstwhile hockey league. This is DPS Kalinga.


[User] Please transcribe this text

[Assistant] And coming all the way from Ahmedabad, the city of India's premier fast bowler, Jaspreet Bumrah, this is Saint Kabir from Narang Pura in Ahmedabad.


[User] Please transcribe this text

[Assistant] Very nice colour. Let's see what we've got with our third team. This is from the city of PR Srijesh. I'm a huge fan of Srijesh. The grid wall of the Indian Hockey team. Every time the team needed him at the Tokyo Olympics, Srijesh was there. From the city of Kochi.


[User] Please transcribe this text

[Assistant] This is the team from Chinmaya Vidyalaya, Kannamali.


[User] Please transcribe this text

[Assistant] Good to see our teams coming from all corners of India. So let's start with you, DPS Kalinga. Tell us a little more about yourself.


[User] Please transcribe this text

[Assistant] You know, I'm delighted to hear that because India must become a truly multi-sport country. Right, what about you?


[User] Please transcribe this text

[Assistant] Okay, Kochi, Ahmedabad, Kalinga, we're all set. 27 teams have started this journey, represented by 54 brilliant young minds. In each of the rounds that feature three teams, the winner moves to the semi-final stage, inching towards...


[User] Please transcribe this text

[Assistant] Being crowned the first ever champion of the Ultimate Sports Quiz 2022. Isn't that fantastic?


[User] Please transcribe this text

[Assistant] Now apart from being awarded this magnificent trophy, that champion school gets a sum of rupees 35 lakhs. The team that's placed second gets 20 lakhs, the third finalist gets rupees 10 lakhs, but they need to use that money to develop sporting infrastructure in their schools. It's a little way of saying,


[User] Please transcribe this text

[Assistant] We are contributing to building a truly sporting nation. But also,


[User] Please transcribe this text

[Assistant] Finalists of the Ultimate Sports Quiz 2022. Big dreamers of this contest, aren't they? They're in for a hearty reward. A reward that'll make their dream of watching their favorite sport live come true.


[User] Please transcribe this text

[Assistant] It's an all-expenses paid trip sponsored by Dream Set Go. A world where your dream of watching


[User] Please transcribe this text

[Assistant] Any leading sporting game live is tailor-made into an unforgettable experience. So let's begin that journey for three wonderful teams today.


[User] Please transcribe this text

[Assistant] Let's get into our first round. It's called Sitius.


[User] Please transcribe this text

[Assistant] Okay, see this is all about grids, but within that there's a method to it.


[User] Please transcribe this text

[Assistant] What you will see is a seven by seven square. There's a letter made. Five names within that that you have to find for five points each. They could be left to right, top to bottom.


[User] Please transcribe this text

[Assistant] First names, last names, sports persons, or just sporting related terms, all related to a theme. Now, if you get all five correct,


[User] Please transcribe this text

[Assistant] You get 5 points as a bonus too. So that's 30 points. And then...


[User] Please transcribe this text

[Assistant] You can earn another 10 points bonus if you answer the connection question right. Remember though that this bonus question


[User] Please transcribe this text

[Assistant] Could pass on to the others as well if the assigned team fails to answer it. And in this round, there are no negative points.


[User] Please transcribe this text

[Assistant] So please go for it. Okay, so if all of you are ready, let's get into our first grid. DPS Kalinga, French Open Women's Champion.


[User] Please transcribe this text

[Assistant] Brady, run out of time. You said H-A-L-E. Did you miss a consonant there? Think you were looking for Simona Halep. But what about the one you did not get?


[User] Please transcribe this text

[Assistant] Let's see as it comes up, let's capture their expressions as it comes up.


[User] Please transcribe this text

[Assistant] Doesn't matter, you got some correct, so you've got your scoreboard moving. Here, DPS Kalinga is your bonus question.


[User] Please transcribe this text

[Assistant] Which of these players that you see over there has only won the mixed doubles title at the French Open?


[User] Please transcribe this text

[Assistant] Who went on the buzzer first? Yes, Chinmavidyalaya. Sanya Mirza. Sanya Mirza is absolutely right. She won the mixed doubles. All the others, if you look down there, they've had, uh, they've had single swings as well. So, that was the answer I was looking for. Let's go into our next grid and this goes to Saint Kabir Ahmedabad.


[User] Please transcribe this text

[Assistant] And you got that bonus five that comes with, so you got your 30 points, here is your bonus question.


[User] Please transcribe this text

[Assistant] Toronto Raptors. You didn't need to hit the buzzer because the question was yours in the first place. Toronto Raptors has to be. Well done, you've got a clean sweep of that. So you've got a little healthy lead up there.


[User] Please transcribe this text

[Assistant] Okay, let's get into our next grid, which is Chinmay Vidyalaya Kochi.


[User] Please transcribe this text

[Assistant] You said picture. Excellent answer. That gives you 25 plus the bonus 5 points. And that sets you up very well for the bonus question that is coming up now.


[User] Please transcribe this text

[Assistant] Which of these sporting rules is associated with baseball? Pitcher. Is indeed the pitcher. Well done. Pitcher.


[User] Please transcribe this text

[Assistant] That was wonderful, isn't it? We got some good answers, we got some answers, we didn't get some answers that were on the tip of their tongue but that's all part of it because CTS is all about speed and speed can cause you to make the odd mistakes sometimes. Let's see where we are on the scoreboard then.


[User] Please transcribe this text

[Assistant] DPS Kalinga, I think the next round's gonna be yours. You started off really well. Now, at the moment, there's a great contest. Saint Kabir on 40 and Chinmay Vidyalaya, because they swept that first round, are sitting pretty at the moment on 50.


[User] Please transcribe this text

[Assistant] So there you are, there's still lots of questions to come, lots of points to be earned. We'll take a break. When we come back, we're going to play All Tears.


[User] Please transcribe this text

[Assistant] What are you going with DPS this time? Virat Kohli 20. This is the big move. Here we go.


[User] Please transcribe this text

[Assistant] This is the Ultimate Sports Quiz 2022. We've got three teams from different parts of India. We've seen how good they are at Cityus. Now let's see how good they are as we play Altius.


[User] Please transcribe this text

[Assistant] Why, so this is how we play Altius. Six subjects on display on a grid to choose. Each subject though has three questions embedded in it. The questions are worth five, ten or twenty.


[User] Please transcribe this text

[Assistant] We start with a team that's trailing at the end of round one. They pick their subject first, and then we go along. If they pick a five-pointer, there's no negatives. If they pick a ten-pointer, there's a minus five attached to it. If they pick a twenty-pointer, there's a minus ten attached to it, in case the answer is wrong. However, if they pick a ten or a twenty-pointer, and they think they don't know the answer, they can pass, they won't get negative points on it. But, if you've, uh, hit the buzzer, if you've asked for it, then you get it wrong, you get negative points. I think that's, uh, pretty simple.


[User] Please transcribe this text

[Assistant] Okay, it's time to start. Let's start at the moment with the team that's currently placed third.


[User] Please transcribe this text

[Assistant] We'll start with you, DPS Kalinga. Let's take a look at the grid.


[User] Please transcribe this text

[Assistant] Okay, those are the six topics that you've got. Virat Kohli. Sir, Virat Kohli. Virat Kohli, okay. Ten points. Ten points on Virat Kohli. They want to do a bit of a catch up. Here is your ten pointer.


[User] Please transcribe this text

[Assistant] What is gesture to stop the fans from booing which Australian cricketer returning from a ban?


[User] Please transcribe this text

[Assistant] Did Virat Kohli win the ICC Spirit of Cricket Award in 2019? Which cricketer are we talking about? Steve Smith. Steve Smith, yes! Steve Smith is absolutely right. Let's go on to Saint Kabir from Ahmedabad. Right, what have you chosen Saint Kabir? Virat Kohli. Virat Kohli again, okay. Five. Five? Okay, let's go five on Virat Kohli. What nickname?


[User] Please transcribe this text

[Assistant] Was Virat Kohli given in his Ranji Trophy days due to his chubby cheeks? Chiku. Chiku, yes!


[User] Please transcribe this text

[Assistant] And some of his closest friends still call him Cheeku. Unfortunately, there are no cheeks to pull anymore.


[User] Please transcribe this text

[Assistant] Because Virat Kohli has become such a fitness fiend. I saw him in a queue once. I saw my plate and I saw his plate. And I thought I know why he looks like he does and I look like I do. But the fitness has taken him to a different level.


[User] Please transcribe this text

[Assistant] And I hope he's able to, you know, with the spirit of fitness, get every young person. Fitness takes you to a different level, but well done.


[User] Please transcribe this text

[Assistant] You've got yourself five points. Right, let's go on the grid now to Chinma Vidyalaya Coach. Your lead's been cut to five.


[User] Please transcribe this text

[Assistant] But you've got your own question in hand. What are you going for on our grid?


[User] Please transcribe this text

[Assistant] Well done, Chinmay Vidyalaya Kochi, pick up five points. Let's go back now, on the grid, to DPS Kalinga.


[User] Please transcribe this text

[Assistant] Virat Kohli is the only cricketer to achieve which test batting feat twice. Only Rahul Dravid, Michael Clarke, Donald Bradman, Brendan McCallum and Ricky Ponting have achieved once.


[User] Please transcribe this text

[Assistant] Okay, on the buzzer, anybody? No? Okay, nobody's got it. Let's take a look at the answer.


[User] Please transcribe this text

[Assistant] Three or more double hundreds in a year. It's a very rare thing to do. So there you go, DPS Kalinga took a...


[User] Please transcribe this text

[Assistant] Bit of a gamble, didn't quite come off, but there's lots more points left. Let's go to St. Kabir Ahmedabad.


[User] Please transcribe this text

[Assistant] Grand slams for ten for Saint Kabir. Here's your question. Which Spaniard beat the Argentine Mariano Puerta?


[User] Please transcribe this text

[Assistant] Saint Kabir, well done. You went 10, you got 10. And that means that you've drawn level with Chinmay Vidyalay. I'll be very keen to know how you're going to play it. Let's get the grid up for you again.


[User] Please transcribe this text

[Assistant] What's your choice? Sports women. Sports women, okay.


[User] Please transcribe this text

[Assistant] Fine. Okay, Chinmay Vidyalaya, going five points with sports women. Here's your question.


[User] Please transcribe this text

[Assistant] Which female Swiss icon has also won Grand Slam doubles titles with Sanja Mirza and with Leanne De Pez?


[User] Please transcribe this text

[Assistant] You got half of it right, but I can't give you a half mark. It's the other Martina. Martina Hingis.


[User] Please transcribe this text

[Assistant] Martina Hingis is the answer you were looking for. Okay, there you go. Chinmay Vidyalay couldn't answer that. And what that means is our leaderboard is now very deliciously placed. Let's take a look at the scoreboard.


[User] Please transcribe this text

[Assistant] And good to see DPS Kalinga did get some points and moved up to 25. But look what's happened at the end of that round of all-tears. Saint Kabir Ahmedabad on 55. Chinmay Vidyalaya Kochi also on 55. And so there's everything to play for in our next round. But before we go there, here's a question for you as well. In chess, what term is used to refer to opening with these moves? D4, D5, C4.


[User] Please transcribe this text

[Assistant] Just to give you a little hint, a mini-series of the same name, starring Ania Taylor-Joy, was released in 2020. You've got a little bit of time while we're in the break, we'll come back with Forte years after.


[User] Please transcribe this text

[Assistant] Two questions left. Saint Kabir watching you very closely. Chinmay Vidyalaya interested to see what you do. Yes, question number nine.


[User] Please transcribe this text

[Assistant] Welcome back, this is the Ultimate Sports Quiz 2022, where I think...


[User] Please transcribe this text

[Assistant] We've got a grandstand finish ahead of us, but while we wait for them to deliver this kind of finish that I hope they will, I wonder if you got your question right. This question of course was, in chess, what term is used to refer to opening with these moves? You go to d4, d5 and then c4, and the answer of course is Queen's Gambit. But at the moment,


[User] Please transcribe this text

[Assistant] I think it's the last thing on their minds because they are getting ready to move ahead. They're getting ready to play Forteus.


[User] Please transcribe this text

[Assistant] Ah, what are the rules for this round? One team member has to step aside and sit on the bench while the other plays. The team captain decides which of the two will play. Ten points for a correct answer, minus five for a wrong one. That's applicable even on the buzzer. After five questions, captain can decide whether they want to swap players or no.


[User] Please transcribe this text

[Assistant] Okay, so let's ask the teams who's in the firing ground, who's not. Let's start with you, DPS Kalinga. Who's playing?


[User] Please transcribe this text

[Assistant] Okay, Sharik is playing. You already sound like Gautam Gambhir, you know. Singh Kabir, Naran Pura.


[User] Please transcribe this text

[Assistant] Who's playing? I am playing. Okay, Rian is playing and Chinmavidyalaya. I'm playing. Okay, Dino is playing. Krishna, you get some time off. So the three of you are not playing. We've got some lovely chairs for you. You can go and watch proceedings from there. So do you want to leave your teammates for a while? Maybe give them a high five, give them a punch and go and stand there. Thank you.


[User] Please transcribe this text

[Assistant] Let's start by playing this round of Forteus. Here's your first question, first attempt. Arch rivals of New Zealand.


[User] Please transcribe this text

[Assistant] Which country's national rugby union team is commonly known as the Wallabies?


[User] Please transcribe this text

[Assistant] Australia. There's Krishna, suddenly putting a hand up and saying Australia. Now, if you were here, you would have to know what a Wallaby was. Okay, let's go to question number two, no change on the leaderboard. In March 2022, against the West Indies at Hamilton, who became the first Indian woman to score three ICC World Cup centuries?


[User] Please transcribe this text

[Assistant] The first Indian woman to score three. Yes, DPS Kalinga. Smriti Mandhana. Question passes.


[User] Please transcribe this text

[Assistant] Okay, Saint Kabir. Mithali Raj. No, it's not Mithali Raj.


[User] Please transcribe this text

[Assistant] Harmanpreet Kaur. It's Harmanpreet Kaur. Chinmay Vidyalay from Kochi gets the carryover points. It is indeed Harmanpreet Kaur. Let's go into question three.


[User] Please transcribe this text

[Assistant] Which football club in the Premier League is managed by Michael Artheta?


[User] Please transcribe this text

[Assistant] Okay, your time is up. Mikhail Arteta, you watched the premiership. Emirates Stadium, earlier Highbury, the Gunners.


[User] Please transcribe this text

[Assistant] Arsenal? It is indeed Arsenal, but your time was up. You won't get the points, but it is indeed Arsenal. So that was question three. Let's go to question four. Which African nation won the 1974 Davis Cup?


[User] Please transcribe this text

[Assistant] After India gave them a walk over in the finals because of prevailing apartheid rules.


[User] Please transcribe this text

[Assistant] Which African nation? Yes, Senkabir. South Africa? Is correct. It has to be South Africa. What else can it be?


[User] Please transcribe this text

[Assistant] Okay, no bids, no answers. The Tour de France, the toughest leg is up the mountains.


[User] Please transcribe this text

[Assistant] Mountains is what I was looking for. It's very, very difficult because when you're going up the mountains on, on that, uh, on that segment of the Tour de France, you get very strong Alpine winds sometimes. It's a very difficult leg because you've got to counter the slope and the Alpine winds. And then if you conquer that, that is a very special part of the Tour de France. So.


[User] Please transcribe this text

[Assistant] Where we are, we've come to the end of the halfway point. Themes, you're happy with who you are? You want to swap? What do you want to do? That's all.


[User] Please transcribe this text

[Assistant] Okay, so Binoy is staying on. What about you, Saint Kabir? I am staying on. Okay, you're staying on. DPS Kalinga is staying on as well. Just for all of you, just so you know, it's 20 to DPS Kalinga. It's 60 to Saint Kabir. It's 65 to Chinmay Vidyalaya. So that's going to determine your strategy as we go into the last five questions. Here is question number six.


[User] Please transcribe this text

[Assistant] Broke Mark Spitz's record of seven Olympic gold medals. Who went first? Chinmay Vidyalay. Michael Phelps.


[User] Please transcribe this text

[Assistant] After 36 years, Mark Spitz of course won seven.


[User] Please transcribe this text

[Assistant] In 1972, we were all children and we all became Mark Spids fans then, and then he tried coming back many years later, but of course in sport, like swimming, there is, there are no comebacks. Let's go to question number seven, four left. Avni Lekhara.


[User] Please transcribe this text

[Assistant] Right, so I'm delighted to see you're still in the contest. The answer, of course, was Avni Lekhara won gold and bronze at the Tokyo 20 Paralympics in shooting. Three questions left. Here's question number eight.


[User] Please transcribe this text

[Assistant] Manish Pandey became the first Indian to score an IPL century while he was playing for which team? Right, Sen Kabir, you went first. Sunrises, Hyderabad. Question pass is incorrect. Rajasthan Royals. Question pass is incorrect. Ah, you're sitting on that, aren't you? You're sitting on that.


[User] Please transcribe this text

[Assistant] He played for the Sunrisers Hyderabad for a long time.


[User] Please transcribe this text

[Assistant] Played for the Kolkata Knight Riders as well. Let's see if you've got the answer up there. Bangalore. Royal Challengers, Bengaluru, what's the answer?


[User] Please transcribe this text

[Assistant] There's two questions left. Saint Kabir, watching you very closely. Chinmavidyalaya, interested to see what you do. Yes, question number nine.


[User] Please transcribe this text

[Assistant] Host the home Grand Prix of which current Formula One World Champion.


[User] Please transcribe this text

[Assistant] Yes. Max Verstappen. Is an outstanding answer. Max Verstappen is an outstanding answer. Well done, DPS Kalinga. And here is our last question for this round.


[User] Please transcribe this text

[Assistant] Who became the first Indian woman to win a medal at the Olympics? Yes. Saint Kabir. Karnam Maleshwari. Is the correct answer.


[User] Please transcribe this text

[Assistant] And just for the benefit of all the simple people who have to listen to the whole question before they can give the answer.


[User] Please transcribe this text

[Assistant] In 2000, who became the first Indian woman to win a medal at the Olympics when she claimed bronze in weightlifting? The answer was, of course, Karnam Maleshwari. A little bit too late for you, Saint Kabir, but I think I'm delighted because we've ended so strongly with two wonderful answers. Can I request all of you to come back again and join your teammates?


[User] Please transcribe this text

[Assistant] Right, come along DPS, uh, DPS Kalinga, Saint Kabir, Chinmay Vidyalay, because it's time for us now to take a look at the final leaderboard for today.


[User] Please transcribe this text

[Assistant] You bolstered your reputation with that last answer. Very good, delighted for you. 35 points.


[User] Please transcribe this text

[Assistant] Saint Kabir, little charge at the end, just fell a little bit short. 65 is a very good score, Saint Kabir. And the two quiet people, 9th standard and 6th standard, very, very well done. Dinoy and Krishna from Chinmay Vidyalaya, Kandamalli, Kochi. Well done.


[User] Please transcribe this text

[Assistant] So that's it for today, but for more sporting knowledge action, catch the next show with three brand new teams from some other zones in the country. So goodbye, keep watching, keep playing sport.


[User] Please transcribe this text

[Assistant] Yeah, what do you think? I had to get my tea aka my coffee for this one. Wait, what's in there for real? This is, this is coffee. This is really interesting at this point. You know why? Because now you got multiple people involved now. So now I'ma just throw it out. If Deshaun Watson get a year, what do the Texans get? You in the lawsuit now, bro?


[User] Please transcribe this text

[Assistant] Yeah, yeah, yeah, yeah. This is what for you. So now you joined at the hip. So what are you gonna do? You're gonna go through with it. You're gonna kill the story. I'm just trying to see what's gonna happen because right now the deep pockets is in a lawsuit.


[User] Please transcribe this text

[Assistant] I tell you what, to me, this guarantees Deshaun Watson, to me, when they added the Texans, this guarantees Deshaun Watson doesn't get a year. Does not get a year.


[User] Please transcribe this text

[Assistant] I, you know, I wouldn't be completely opposed to it for, if Deshaun Watson is suspended for a whole year, I'm not completely opposed to it. You know why? Because that gives Baker a whole year and Deshaun Watson is not in the mix.


[User] Please transcribe this text

[Assistant] So you are, there's no possibility of a comparison. He's here for the year. It is what it is. Deshaun Watson is not here. He's not showing back. It's a clean break. We good. He goes somewhere else. You do anything. But if it's anything less than that, I can't have the comparison because here's what the reality of it is, man. The fan base is still split.


[User] Please transcribe this text

[Assistant] There's a huge racial component of it. Definitely is. And so let's talk about the, see if we can get to this. Keeping it real. The racial component will be, if Baker Mayfield steps back,


[User] Please transcribe this text

[Assistant] Into the, into the saddles. And he goes six and two in eight games.


[User] Please transcribe this text

[Assistant] I'm gonna tell you what. You know Baker Mayfield is more popular than Bernie Cozart, right?


[User] Please transcribe this text

[Assistant] This dude is the best thing we've had. We not talk. You can say young minds, but you could not have


[User] Please transcribe this text

[Assistant] I'm 40 years old. So if you're talking about Bernie, you like 9, 10, 11, 12 years old. When Bernie got moved to Dallas, there was people that was upset, right?


[User] Please transcribe this text

[Assistant] When Baker Mayfield, when they came out and they said they was going after Deshaun Watson, I have never heard.


[User] Please transcribe this text

[Assistant] Baker Mayfield, when you look at the national leads, they lead with Baker. If you skip Shannon, they lead with Baker.


[User] Please transcribe this text

[Assistant] You know what's the most disrespectful thing about it? We talk about Miles Garrett. People always be saying like, you know, that guy's terrible. My grandmother complained. Listen, your grandmother would die.


[User] Please transcribe this text

[Assistant] Your grandfather would die. Your grandmother, your father, the mini fan, your father, anybody you know, you can't, you know, you can't run away from offensive lineman. You know how that's how fast they are.


[User] Please transcribe this text

[Assistant] Oh my god, he's, he's ready to, to retire. All jokes aside, I think, I think the only athlete, like, because this is mythical, the only athlete that I've ever seen that was like that was like Bo Jackson. So Bo Jackson, he was just... Wait, look at that picture. Look at that, we haven't seen this one yet. I mean, but, but for a defensive end,


[User] Please transcribe this text

[Assistant] And even when you compare them to other, when you look at other elite defense events that are all time greats. Look how big Reggie White was. Bruce Smith was, no definition. When you take a look at it, not like that.


[User] Please transcribe this text

[Assistant] Get him on offense. Here's what I'm gonna do. And I'm gonna tell you exactly what they would do. You put Myles Garrett on defense, or excuse me, offense, and he's a lead blocker. They're going low. You're not allowed to.


[User] Please transcribe this text

[Assistant] And that's what I said. But here's the difference between defensive linemen use their hands. When you blocking somebody and when you getting blocked, see I can put my hands down, I can shove, I can get, re-react, get people off me. You ain't used to having nobody underneath your legs. You misunderstood my


[User] Please transcribe this text

[Assistant] Man, I don't want no helmet to helmet hits. I don't want none of that. I want that man to get quarterbacks, not to get hit. I don't want nobody.


[User] Please transcribe this text

[Assistant] You would be, can you imagine this? We're playing the Steelers, right? It's the third game of the year. Miles Garrett goes in on the goal line play and all of a sudden, Miles Garrett is down. He sure is getting up really slowly right now.


[User] Please transcribe this text

[Assistant] People nowadays, they, they will never, you can't readily admit because they got that settlement money. That's Greg Williams. You dirty. It's true though. You messed up. I gotta get to my list here, right?


[User] Please transcribe this text

[Assistant] If you got one player since 1999 in their prime. Charlie Fry. No. Odell Beckham Jr.


[User] Please transcribe this text

[Assistant] Now, I'll say this and just to kind of just throw a bow on it. I'm, we, some of us are married here. You're, you're married, right? Uh, yes, happily, of course. Now, here's one thing that I, here's, here's what I don't do. I don't come home and I got two rules.


[User] Please transcribe this text

[Assistant] If you say the word divorce to me, oh no, we can't talk about that because at that point in time, you've already made your mind and you came to that conclusion and I'm not going to say it unless I really mean it. And at this point,


[User] Please transcribe this text

[Assistant] When they said divorce between Baker and the Browns, it's no coming back from that. You might as well just go ahead and just say, let's get ready to get you finish this out.


[User] Please transcribe this text

[Assistant] Mcnuggets, by the way, it's a party foul to be in a bar, a crowded bar, trying to get your pool game off. Go to a pool hall. Yeah, nobody would like those people. Hold on.


[User] Please transcribe this text

[Assistant] Me and you, we can't do throwing axes after dark. None of that. That's not a thing. I'm not gonna be no six foot five black dude with an axe.


[User] Please transcribe this text

[Assistant] Beer pong is definitely, if you grow up in the Midwest, you from Ohio, you had a party school, hey, beer pong is on there. And where's flip cup? I don't see flip cup. I don't see flip cup.


[User] Please transcribe this text

[Assistant] Same type of player. It's a great call. On top of this, two people forget. Sean Rodgers used to just get upset and decide he want to block a kick. How you just block a heel ball?


[User] Please transcribe this text

[Assistant] I think that was his nickname, Freak.


[User] Please transcribe this text

[Assistant] He was the first dude, he came out, like I was in college when Peppers was in college, right? Yeah. And Julius Peppers was the first defensive end that I would watch and be like,


[User] Please transcribe this text

[Assistant] He doesn't have pads on. He has hoop jersey. He has a basketball jersey and shorts on him out on the football field. How does his jersey look like that? Nobody's jersey fit like peppers. Like he was just like a monster. He looked like a beast and you got to think about it. That man came out.


[User] Please transcribe this text

[Assistant] You know that Dequelle about six foot and a half, six one. If he got, if you see Miles Garrett fill in the hole and he had six foot five, you know what's happening. He about to blow him up below his waist. I'm not tackling you up top. If you catch the ball, you're not running me over. That wasn't the question.


[User] Please transcribe this text

[Assistant] I'm gonna give you this. You guys aren't on Shutter Island completely. You have solely moved me in on a life raft. You're in? But I'm only in only if it's a year. But if they give them a full year,


[User] Please transcribe this text

[Assistant] Then to me, that's a full reset. You reset the whole season. And so now you don't feel so bad about saying, all right, we're coming back in here, Baker. Let's figure out what we're going to do for a year. Mutually beneficial. If it's anything less than that.


[User] Please transcribe this text

[Assistant] To develop the players to, to have a consistent place to play in the USFL or is it geared towards getting guys ready for the next level or possibly go another place CFL or, or the NFL? What is the, what is the structure of the business model look like as far as player development?


[User] Please transcribe this text

[Assistant] Well, one great memory coach is, is, uh, when I was growing up and, you know, obviously, um, when you watch quarterbacks, you watch Randall Cunningham, uh, you watch quarterbacks like that. And the first guy that comes to my mind is Steve McNair and, and just watching, you know, Eric McNair and, and, and,


[User] Please transcribe this text

[Assistant] Him coming from a historically black college or a small school and seeing him be like a top pick. Talk a little bit about Steve McNair, how, how you guys, uh, you know, went and drafted him. What, what, what went into that and a good story about Steve McNair and what he did for the Tennessee type.


[User] Please transcribe this text

[Assistant] When they say Jameel Miller, all I can think of is Dwayne Ware throwing that helmet.


[User] Please transcribe this text

[Assistant] That's great. By the way, I gotta, I gotta give the OG credit for that. We had a, after meeting, Brad says, hey guys, I met somebody in the gym and they saying, I'd love to talk about that. Where are the women at? So I'll give you credit for that too.


[User] Please transcribe this text

[Assistant] I can't fix it for you, Jerry. You'll have to talk to yourself.


[User] Please transcribe this text

[Assistant] I can't remember when I heard a more stupid remark.


[User] Please transcribe this text

[Assistant] I do a little dye job because it was the first time I had ever had a beard in my life. Basically because I couldn't grow one until I was 40. And then by the time I got to 40, then all this other stuff started showing up.


[User] Please transcribe this text

[Assistant] Alright, so obviously this has been a very, uh, difficult situation for lifelong Browns fans like myself, right? Sure.


[User] Please transcribe this text

[Assistant] boys that are 21 and 16. We have a lot of talks over the years about when you have a passion for a team, you're not going to


[User] Please transcribe this text

[Assistant] Uhm, heading into the season probably since the mid eighties, mid to late eighties, when it was really, I remember one preseason game, it was probably heading into the year where we lost


[User] Please transcribe this text

[Assistant] the drive. I'm going to s So it was the year after where we lost the big lead


[User] Please transcribe this text

[Assistant] Well, I would say Browns because when I was growing up, Jay, much like you, uhm,


[User] Please transcribe this text

[Assistant] I was at game, you know, all the games, obviously the 2016 World Series covering it.


[User] Please transcribe this text

[Assistant] Uhm, and then when we finally won something,


[User] Please transcribe this text

[Assistant] It was on Father's Day in 2016. We had a big party over here. And I remember hugging my sons. The oldest one was just shy of 16. The younger one was 10. And I was like, savor this.


[User] Please transcribe this text

[Assistant] Talent-wise, he's Kyrie Light, and I don't mean that in a negative way. I think Kyrie's one of the most skilled players that we've ever seen in this game. So, I think...


[User] Please transcribe this text

[Assistant] They are really, really close to being in the thick of that Eastern Conference, and that is a good conference.


[User] Please transcribe this text

[Assistant] Totally. Yea, and you want, like one of my favorite things in sports is when guys from


[User] Please transcribe this text

[Assistant] Other teams in the same city rally around those teams because they're not from Cleveland. Most of the guys that played for Cleveland sports team aren't from Cleveland. Like last night, Ronald Acuna Jr., who's one of the stars of the Atlanta Braves, has a multi-homer night and he does the whole shout out to Trey Young with his ice tray.


[User] Please transcribe this text

[Assistant] Well, alright, second topist. I guess I would say the AFC West, just because...


[User] Please transcribe this text

[Assistant] I really believe it would be smart for both the organization and Baker Mayfield to get in a room and sit down and talk things out.


[User] Please transcribe this text

[Assistant] Guys, Jimmy Garoppolo watched the San Francisco 49ers trade three first round picks for a kid that played at North Dakota State. They were in the same quarterback room.


[User] Please transcribe this text

[Assistant] But at some point somebody's gotta sit him down and say, you're at your best when nobody believes in you.


[User] Please transcribe this text

[Assistant] Right. This is kind of breaking news. It's not missing a few OTAs. Coaches can say as much as they want, hey man, we need everybody here. You know, we want it.


[User] Please transcribe this text

[Assistant] If it's half a season, you know, if it's eight games, uh,


[User] Please transcribe this text

[Assistant] He really did some damage at the edge. I like him a lot.


[User] Please transcribe this text

[Assistant] I had never seen such a strong man cry like that before.


[User] Please transcribe this text

[Assistant] Well I'm Jason Lloyd and there's one thing that came out about the Shawn Watson this week that I'm...


[User] Please transcribe this text

[Assistant] Concerned about that I think could shut this whole thing down. We're treading toward a very dangerous line and I'll tell you what it is coming up.


[User] Please transcribe this text

[Assistant] We have more information. It's unclear.


[User] Please transcribe this text

[Assistant] If the league has more information, if the Browns have more information, is it feasible that something came out of the New York Times article that they didn't know? Yeah, I think that's probably pretty feasible. But we don't know. Like, we don't know what they knew. Everyone has been real cloak and dagger with this. So it's hard to say that there's new information that's emerged. To us there is, yeah.


[User] Please transcribe this text

[Assistant] They may have already known about it. They may have already known about it. We don't know. Roger Goodell made it very clear at the owner's meetings. The commissioner's exempt list is off the table as long as the criminal component is gone. And when he got the no bill on the two grand juries and there was no indictment coming, that left. That's not coming back unless criminal charges. Future indictments are


[User] Please transcribe this text

[Assistant] Yeah, so I don't think he's going on the commission as an exemplist and frankly, I could be way off on this. This is not, this is just my opinion.


[User] Please transcribe this text

[Assistant] I don't think he's going to be suspended for a full year because he's not on the commissioner's exemplist. If, if they were going to suspend him for a full year, then you just put him on the exemplist. And the fact, and maybe I'm connecting dots that I shouldn't be connecting, but the fact that Roger was so emphatic that the commissioner's exemplist is not an option.


[User] Please transcribe this text

[Assistant] As long as there's no indictment and criminal charges coming.


[User] Please transcribe this text

[Assistant] I don't think it's gonna be a year. Right. Now I think I'm becoming in the minority on that. A lot of people seem to think it's gonna be a year, maybe two years or whatever. I've heard some wild numbers thrown out. Right. I still think it's gonna be less than a year.


[User] Please transcribe this text

[Assistant] I'm just telling you as of a week ago, two weeks ago, before the New York Times article and Rusty Harden put his foot in his mouth last week, before some of that other stuff, the Browns were fully expecting to have a decision made by the start of training.


[User] Please transcribe this text

[Assistant] You lose, the thing that loses me on, he wants to clear his name and he denies doing all wrong, is when Rusty Harden goes on,


[User] Please transcribe this text

[Assistant] The Between the Lines podcast by attorney Gabe Feldman before he did what I was on with 92.3 the week before that.


[User] Please transcribe this text

[Assistant] He goes on and says, basically, the Dolphins were ready to trade for him. Deshaun wanted to go there. The owner, Steven Ross, said, clean all these up and get rid of them. And they were more than happy to do that. They were more than happy to clean these up and get rid of them. He wants to play. And it broke down. So let's, let's, let's just retire the fact that Deshaun is adamant on clearing his name.


[User] Please transcribe this text

[Assistant] He's not. He's more than willing to settle these suits if the parameters allow it.


[User] Please transcribe this text

[Assistant] I think it goes back to what we were just talking about. Like he's more than willing to settle. And that's been proven a couple of times now. And you know, I think...


[User] Please transcribe this text

[Assistant] Maybe Rusty got a little bit too cute with the NDAs and we're saying like they didn't want any NDAs, they wanted everyone to see how little money they got, the women got, and Busby was pushing. And Rusty did tell me that too, that, you know, Busby pushed back on the NDAs because he was probably embarrassed by how little the money was.


[User] Please transcribe this text

[Assistant] Well, okay, now I think we're getting into sort of a, a measuring contest here. Just settle. You know, just, and, and NVAs are important.


[User] Please transcribe this text

[Assistant] No, this team hasn't been to Super Bowl in the existence of Super Bowl. So I can't put that as the bar that they have to win at Super Bowl or it's a terrible trade.


[User] Please transcribe this text

[Assistant] There's no way you can guarantee there's seven, there's in my mind, there's seven elite quarterbacks. That doesn't include Derek Carr, who's pretty close to elite as far as I'm concerned. And that doesn't include the Titans and the Steelers. There's 10 teams. There's no guarantee they even make the playoffs with this guy.


[User] Please transcribe this text

[Assistant] And he is the missing piece. And I don't think the Browns regret the trade because I was at, you know, I've talked to enough people in the organization all throughout who felt like,


[User] Please transcribe this text

[Assistant] Baker was not it. And they had to upgrade. And Baker's arm wasn't good enough. And, and they just, it just, it just wasn't there.


[User] Please transcribe this text

[Assistant] I think they would do it, yes. But I've also said, I've had people agree with me, if he's out for an entire year, this is a disaster. Oh, there's no question. I don't agree with that at all. Oh, it's a disaster. Because of what they gave up. Because of the contract.


[User] Please transcribe this text

[Assistant] Jason is two years out of line. For Deshaun? That's absurd.


[User] Please transcribe this text

[Assistant] I don't think he's going to get two years. Like, I don't think he's going to get two years. So I don't want to say what's out of line or not.


[User] Please transcribe this text

[Assistant] It just makes it, this whole thing just makes me really uncomfortable.


[User] Please transcribe this text

[Assistant] I will go back to what I said before. Six to eight games is what I've thought all along. Here's the thing.


[User] Please transcribe this text

[Assistant] And this is what I teased at the start. We're getting dangerously close though to one area that will shut this whole thing down and will bring the criminal component back. In that New York Times story, there's a physical therapist who is not suing him. That's important. She's not part of this lawsuit. Who said that basically twice,


[User] Please transcribe this text

[Assistant] That's the closest that we've come. That part of the New York Times article is the closest that we've come to that happening.


[User] Please transcribe this text

[Assistant] But I'm just saying we are trending toward a line here that could derail this whole thing. So I'm not saying it's ever going to happen.


[User] Please transcribe this text

[Assistant] That's a huge difference. Yes. I'm just pointing out, I've said all along, if someone comes out with rape allegations, everything changes. Sure. I think everybody would agree with that in a heartbeat. And we're getting, we're getting closer to that line with that.


[User] Please transcribe this text

[Assistant] But a lot of people are- I killed them for it. Because they weren't converting on them. And there were times as the year went on last year where I likened it to-


[User] Please transcribe this text

[Assistant] Holding on 16 at Blackjack. The book says you hit. And the book would say to go for it on some of these. And as the year went on and they kept failing on 4th down, I felt like a couple times he held on 16 because he had faith in his quarterback.


[User] Please transcribe this text

[Assistant] I've said it a million times, I think the shoulder injury was way overblown. I think people made way too big of a deal out of the shoulder injury. I got so sick of listening to it. Embarrassing. And, and so, you know, but for whatever reason. There was an injury that needed surgery and it happened in week two.


[User] Please transcribe this text

[Assistant] Well, yes, the injury occurred in week two, but it got worse later on. But still, I'm just telling you, I think we made way too big of a deal about it. They were calling quarterback sneaks in games where if his shoulder was as bad as it was, they would not be calling those plays. And I asked Kevin a hundred different times, a hundred different ways.


[User] Please transcribe this text

[Assistant] Baker had the most success the year before rolling left and getting out of the pocket and the move. That is gone. Why is that gone? Are defenses taking it away or are you protecting the shoulder? And he said a hundred different ways the shoulder has nothing to do with it. And it was, it was the way defenses were scheming them. And that's why they had to move on. And that's what happens in the league.


[User] Please transcribe this text

[Assistant] I think this decision went even higher than Kevin Stefanski.


[User] Please transcribe this text

[Assistant] I think it went all the way to the very top of they needed to see what they had in six because they had to figure out if they were going to extend him or not. Right, you can't tell if he's on the sideline. You can't tell if he's on the sideline and that's a huge reason why he played.


[User] Please transcribe this text

[Assistant] When he went from the bike-a-thon to starting the academy.


[User] Please transcribe this text

[Assistant] And not that, not the school academy, but when he started his I promise program, that is when things really went in motion. And the timing of it was, he was a little skeptical like, okay, well is he just doing this to try and cover the PR of going to Miami? No.


[User] Please transcribe this text

[Assistant] What he is doing, it's remarkable. And it is from the heart too. He's made programs for parents of kids in his, I promise, so that they can get GEDs. I've sat in these people's homes. I've talked to the parents. I've talked to the kids of the lives that he's changing. The stuff he's, I've said this, I've said this for years. His influence and his impact off the court.


[User] Please transcribe this text

[Assistant] They keep them, they keep them so isolated from us now. I really, I mean. It's different. Yeaah, I mean we hear things, but to see it.


[User] Please transcribe this text

[Assistant] I actually would have taken bad bet on this cause he's only gone over 26 once in the series. I know they're at home. Uh, I, I'm actually a G. I think gold state wins the night. I do. I'm not ready. I'm not ready to write the obituary.


[User] Please transcribe this text

[Assistant] You're gonna go- You got nothing though on this one? I- I know just enough about power tools to be dangerous. Okay. How about that?


[User] Please transcribe this text

[Assistant] I mean, I grew up in the newspaper industry. It has to be newspapers for me. It's still delivering a newspaper, having it in my hand. Yep, I'm the same way. Newspaper for me.


[User] Please transcribe this text

[Assistant] So, uh, hi everyone, uh, my name is Naveen. Uh, so today we'll be talking about, uh, the paper Swin Transformer, Hierarchical Vision Transformer using Shifted Windows by Liu et al.


[User] Please transcribe this text

[Assistant] They are members of Microsoft Research Asia. So, let's begin the presentation now.


[User] Please transcribe this text

[Assistant] So, uh, previously in this class, we discussed, uh, what transformers are, uh, what is self-attention in the transformers, uh, architecture and how they beat, uh, LSTMs in natural language processing, et cetera.


[User] Please transcribe this text

[Assistant] And last week we discussed vision transformers and their robustness.


[User] Please transcribe this text

[Assistant] So there will be a kind of recap here. So let me walk you through the memory lane to


[User] Please transcribe this text

[Assistant] Refresh these concepts again briefly. Uh, this is particularly important for our paper as well to set the background. Uh, as we get to understand how the idea of Swin transformer came about.


[User] Please transcribe this text

[Assistant] So, uh, like, what are transformers? Like, we, uh, know, we all saw this, uh, architecture, model architecture, uh, so this is, uh, transformer architecture block.


[User] Please transcribe this text

[Assistant] So this is an encoder, this is a decoder. So the core part of this architecture is self-attention layer, which is here.


[User] Please transcribe this text

[Assistant] So, uh, the question is, uh, with this, uh, architecture of, uh, transformers, like how did transformers beat LSTMs in natural language processing?


[User] Please transcribe this text

[Assistant] So for that we'll take a quick look at the drawbacks of LSTMs in the first place.


[User] Please transcribe this text

[Assistant] Uh, so, uh, LSTMs were first, uh, slow to train, um, like recurrent neural networks are already slower to train because we need to provide data sequentially while training instead of parallely.


[User] Please transcribe this text

[Assistant] A hidden state would require input from all the previous words to make any progress. So this kind of architecture doesn't take advantage of


[User] Please transcribe this text

[Assistant] Today's GPUs and massive parallel processing, because GPUs are optimized for like parallel processing and coupled with the added complexity in an LSTM in the form of multiple gates, etc.


[User] Please transcribe this text

[Assistant] Uhm, like LSTMs are even slower to train compared to RNMs.


[User] Please transcribe this text

[Assistant] And the second weakness is low contextual awareness. Uh, normal LSTMs, uh, process the words in, uh, in a single direction, in one direction, maybe left to right. And, um, this limits the contextual awareness of the network.


[User] Please transcribe this text

[Assistant] Uh, even the bidirectional LSTMs which, uh, learn the context in both forward and backward direction and concatenate them.


[User] Please transcribe this text

[Assistant] Uhm, a better approach would be to look at both sides, like, uh, together at the same time.


[User] Please transcribe this text

[Assistant] And, uhm, the third, uhm, drawback of LSTMs is they are very bad at long sequences.


[User] Please transcribe this text

[Assistant] We already know that LSTMs can perform better than RNNs due to their presence of gates. But even with this, the improvement in the case of LSTMs is not that significant when we are trying


[User] Please transcribe this text

[Assistant] Uh, tasks, uh, that, um, that maybe like a large number of sentences, for example, uh, text summarization and, um, maybe questions and answers, et cetera.


[User] Please transcribe this text

[Assistant] So uhm, now that we have taken a look at some of the issues with LSTMs, we'll see how the transformers architecture elevates these problems.


[User] Please transcribe this text

[Assistant] So uhm, in a nutshell, like first there is an encoder block, right, in a transformer. So uhm, an encoder tries to understand the context of an input sentence and in the process learns what the language is.


[User] Please transcribe this text

[Assistant] Let's see how the data moves in encoder. The input to an encoder is a sentence. These individual words are first


[User] Please transcribe this text

[Assistant] Uh, tokenized and replaced by pre-trained word embeddings in the input embeddings layer.


[User] Please transcribe this text

[Assistant] In the next step, we add a position encoding to individual word vectors, essentially a function that maps to the position of a word in a sentence.


[User] Please transcribe this text

[Assistant] And, uh, to this, uh, we add our initial input embeddings to make sure that apart from the word, uh, we also have a position aspect of it embedded in, uh, while we pass the sentence into the encoder.


[User] Please transcribe this text

[Assistant] So uhm, next we pass this to a self-attention layer. The idea here is to create a matrix of importance of each word.


[User] Please transcribe this text

[Assistant] Uhm, while defining context of other words in the same sentence. So each word would obviously have significant importance with itself and then with other words.


[User] Please transcribe this text

[Assistant] And finally, we, uhm, pass it, uh, through a feed forward layer, which is, uh, basically a dense neural network layer.


[User] Please transcribe this text

[Assistant] And uhm, the biggest advantage of this transformer network is that we don't need to pass the data sequentially, but we can pass the data in parallel. This greatly improves the training time.


[User] Please transcribe this text

[Assistant] And since we are looking at the words in an oral manner, like not in a particular sequence, one after the other, uhm, and that too without any directional sense, the oral understanding of the language or context is much better than other lectures.


[User] Please transcribe this text

[Assistant] So here we can see an example of a sentence where attention is playing a role. So here we can see that the darker the red, the higher the attention each individual word has with all the other words. So we can clearly see that when processing the word the,


[User] Please transcribe this text

[Assistant] So highest attention, uh, is paid to, uh, the most relevant word that is dog by the model.


[User] Please transcribe this text

[Assistant] Similarly, when processing the word big, the model learns that the most relevant word here is dog. And similarly for other sentences, like for other words in the sentence too.


[User] Please transcribe this text

[Assistant] Next, we will see what the decoder is. In a nutshell, the decoder takes the output sentence as input and tries to output the next word. For example, in the case of language translation.


[User] Please transcribe this text

[Assistant] So uhm, the first step is similar to encoder. Here, embedding coupled with positional encoding for each output word is the input to the decoder first.


[User] Please transcribe this text

[Assistant] Next, we compute a masked self-attention. This is slightly different from before, as here we are intentionally masking the words that are yet to come. Essentially, we don't provide the words that are supposed to come.


[User] Please transcribe this text

[Assistant] Come next, while calculating the importance of the current word.


[User] Please transcribe this text

[Assistant] And then the input from the decoder network is looked at alongside encoder's input. And finally, as the word travels through the feedforward and linear layer, we reach the softmax.


[User] Please transcribe this text

[Assistant] Uhm, the softmax is, uh, essentially on all, uh, all the words in vocabulary, uh, uhm, uh, which gives us a probability score for all the words in the dictionary for the next word. And the highest probability word is chosen for each position and this process happens in a parallel.


[User] Please transcribe this text

[Assistant] So uhm, now that we got an understanding about, uh, how, uh, a brief understanding about how a transformer works for natural language processing and or comes, uh, the disadvantages of, uh, LSTMs.


[User] Please transcribe this text

[Assistant] We can ask a question like, how do you take this concept and apply it to a vision domain?


[User] Please transcribe this text

[Assistant] Uhm, so, uhm, the response is, the answer is, uh, the vision transformer from the paper, uh, images worth 16 by 16 words, uh, that we discussed last week. So, uhm, let's take a brief look at, uh, the vision transformer's architecture.


[User] Please transcribe this text

[Assistant] In the case of natural language processing, say for example, language translation task, we took a sentence, tokenized the words in that sentence and gave them as input to transformers encoded.


[User] Please transcribe this text

[Assistant] By doing this, the model learns the context of each word with respect to each other word in that sentence. But we can do the same thing with an image. Like, it is infeasible to take each individual pixel and make the model learn the context of that particular pixel with respect to


[User] Please transcribe this text

[Assistant] Each other pixel in an image, like it's a quadratic, uh, computation complexity, uh, will arise, uhm, because even for a small, uh, image size of 256 by 256 pixels,


[User] Please transcribe this text

[Assistant] It will lead to 256, um, squared, uh, number of computations.


[User] Please transcribe this text

[Assistant] And the other important thing is each pixel by itself doesn't carry enough meaning. Even to us, it's a group of pixels that make sense. And each individual pixel doesn't make any sense by itself.


[User] Please transcribe this text

[Assistant] So, uh, the others in this vision transformer paper divided each image into a set of patches.


[User] Please transcribe this text

[Assistant] And, uh, input, uh, these, uh, uh, patches into, uh, the encoder, uh, transformers encoder after adding their, uh, patch and positional embedding information here as an input to, uh, the, uh, transformer encoder for classification tasks.


[User] Please transcribe this text

[Assistant] So uhm, let's look at uhm, like a performance of Vision Transformer. Uhm, like Vision Transformer achieved impressive results in image classification despite


[User] Please transcribe this text

[Assistant] Having less inductive bias compared to convolutional neural networks.


[User] Please transcribe this text

[Assistant] We know that convolutional neural networks are tailor-made for vision tasks, but in the VIT paper, the only inductive bias we are giving to the transformer is that we divided input image into some fixed number of patches.


[User] Please transcribe this text

[Assistant] As we know that, unlike natural language processing, calculating each pixel's content, like each pixel's context to each other pixel in the input image is infeasible and not worth the computation actually.


[User] Please transcribe this text

[Assistant] So, even with this low inductive bias, Vision Transformer managed to achieve impressive accuracy in the image classification task.


[User] Please transcribe this text

[Assistant] And the other paper we looked at last week was understanding the robustness of vision


[User] Please transcribe this text

[Assistant] Uh, changes like input perturbations and model perturbations.


[User] Please transcribe this text

[Assistant] Uhm, we know, uh, from, like we studied from the paper results, uh, that VIT model has a good robustness against, uh, these.


[User] Please transcribe this text

[Assistant] And, uh, they studied, uh, they, they mentioned that self-attention possesses a strong inductive bias towards token uniformity.


[User] Please transcribe this text

[Assistant] Um, but with the presence of, uh, skip connections and, uh, multi-layer presser prongs, we, uh, um, they stop for the output from, uh, degenerating exponentially to a rank one matrix. So, um,


[User] Please transcribe this text

[Assistant] Uh, in summary, uh, so far, uh, from what we studied so far, we, uh, we got to know, like, we understood that vision transformers are good and maybe even better than, uh, convolutional neural networks at image classification tasks and, uh, they're robust to, uh, perturbations.


[User] Please transcribe this text

[Assistant] So uhm, is VIT, uh, the holy grail, is VIT all we need?


[User] Please transcribe this text

[Assistant] VATs are very good at, uh, image classification. We have seen that last week.


[User] Please transcribe this text

[Assistant] Uhm, then what about other, uh, vision tasks, uh, such as, uh, semantic segmentation and object detection, et cetera? For, uh, for example, say, uhm, uhm, is the VAT model good enough to perform at par or even beat CNNs in these tasks? Uhm, yet to know.


[User] Please transcribe this text

[Assistant] And, uh, what about the path size? Uh, what is so, um, like sacrosanct about the number 16 or 14? Like, from what we saw last week, it's tied to the image size, right?


[User] Please transcribe this text

[Assistant] So uhm, so what, what happens when we vary the path size?


[User] Please transcribe this text

[Assistant] And what happens if we vary the input image size? From what we saw last week, if the input image size increases,


[User] Please transcribe this text

[Assistant] Uhm, and if we keep the number of patches constant, then the more number of pixels or more information will go into the same patch, right?


[User] Please transcribe this text

[Assistant] Similarly, if image size decreases and if we keep the same number of patches, it will result in each patch not containing that many pixels or not containing that much information.


[User] Please transcribe this text

[Assistant] Uh, right, uh, so similarly we can argue, uh, by varying the patch, uh, size like 16, 14 or 32. So, uh, but these are all like hyperparameters, right? But, uh, uh, then how do, uh,


[User] Please transcribe this text

[Assistant] Like, uh, these are the questions like, uh, we'll come like what happens if we increase, uh, resolution of input image or number of patches, et cetera.


[User] Please transcribe this text

[Assistant] So before asking ourselves this question, let me describe briefly, take a detour to describe other vision tasks besides classification that this particular art paper talks about.


[User] Please transcribe this text

[Assistant] A computer vision task in which we label specific regions of an image.


[User] Please transcribe this text

[Assistant] Uh, according to what's being shown like, uh, here, uh,


[User] Please transcribe this text

[Assistant] Given this image, we have to predict that this is a person, like these pixels, uh, belong to a person and these pixels belong to a bicycle and these pixels belong to a background.


[User] Please transcribe this text

[Assistant] So uhm, we are trying to find answers to the question like what's in this image and where in the image is it located.


[User] Please transcribe this text

[Assistant] Specifically, the goal of semantic image segmentation is to label each pixel of an image with a corresponding class of what is being represented. Because we are predicting


[User] Please transcribe this text

[Assistant] For each pixel in the image, this task is commonly referred to as dense prediction.


[User] Please transcribe this text

[Assistant] Uhm, uh, one important thing to note, uh, is that we are not separating instances of the same class, like, uhm, the pixel here.


[User] Please transcribe this text

[Assistant] Uhm, belongs to a different person and this, the pixel here, uh, belongs to a different person. But we are not trying to differentiate between, uh, uh, two persons because they belong, uh, these, uh, these, these pixel and this pixel belongs to same class, a person.


[User] Please transcribe this text

[Assistant] So here we only care about the category of each pixel.


[User] Please transcribe this text

[Assistant] So if we have two objects of the same category in our input image, the segmentation map does not inherently distinguish these as separate objects.


[User] Please transcribe this text

[Assistant] Uhm, like, uhm, for that there exists a different class of models known as, uh, instance segmentation models, uh, which do distinguish between separate objects of the same class, but, uh, here, uh, semantic segmentation doesn't differentiate that.


[User] Please transcribe this text

[Assistant] So uhm, they are useful for tasks such as autonomous vehicles, like for we need to equip


[User] Please transcribe this text

[Assistant] All cars can be categorized as the same class of cars and there is no advantage in distinguishing a car of type, of a type, some say BMW and a car of type, say.


[User] Please transcribe this text

[Assistant] Another category where semantic segmentation helps is medical image diagnostics. Machines can augment analysis performed by radiologists, greatly reducing time required to run diagnostic tests.


[User] Please transcribe this text

[Assistant] I don't know why the images are not showing up, but uhm, we can, like, say for example in a chest X-ray, uhm, we can differentiate pixels belonging to heart and pixels belonging to lungs and clavicles, etc. into various segments.


[User] Please transcribe this text

[Assistant] Let's take a look at object detection next. So image classification here involves assigning a class label to an image where each object localization involves drawing a bounding box.


[User] Please transcribe this text

[Assistant] Uh, like this around one or more objects in an image. So object detection is more challenging and combines the two tasks, like drawing a bounding box around each object of interest in the image and assigning them to a class level.


[User] Please transcribe this text

[Assistant] Uh, so together, um, like these two problems can be referred to as object recognition as well. And, um, uh, for this task, like region-based convolution neural networks are RCNNs are, uh, uh, and their family of techniques, uh, are


[User] Please transcribe this text

[Assistant] Uhm, are actually, uhm, a state of the art before these papers.


[User] Please transcribe this text

[Assistant] They are designed for, like, model performance or CNNs. And, uhm, there are another set of models called, uh, YOLO. You only look once. It's a second family of techniques for object recognition. They are more designed towards speed and real-time use.


[User] Please transcribe this text

[Assistant] So uhm, so what are the challenges faced by Vision Transformer to achieve good results or maybe beat convolutional neural networks on these tasks?


[User] Please transcribe this text

[Assistant] So, let's look at some of them.


[User] Please transcribe this text

[Assistant] So the feature maps produced by Vision Transformer are of a single lower resolution.


[User] Please transcribe this text

[Assistant] Uh, because every patch is of the same size and, uh, this won't work if we are doing, uh, dense prediction, like in, like when you are trying to classify, uh, at a pixel level, whether that pixel belongs to a class person or a class bicycle.


[User] Please transcribe this text

[Assistant] Uhm, so this is one of the, uhm, like drawbacks, uh, if I can, if I may say, uh, about vision transformer. And, uh, the other is, uhm,


[User] Please transcribe this text

[Assistant] To understand the image better at a smaller scale. This won't work because of the quadratic complexity. Because this involves computation of self-attention globally.


[User] Please transcribe this text

[Assistant] Uh, each patch, uh, each patches, uh, context is measured.


[User] Please transcribe this text

[Assistant] Uhm, with respect to each other patch, uh, available.


[User] Please transcribe this text

[Assistant] At a global level, right? So uhm, to answer these uhm, like answering these two questions is the basis of Swin Transformer. We'll see how Swin Transformer solves these problems, but before that we'll try


[User] Please transcribe this text

[Assistant] We can take inspiration from convolutional neural networks to understand how CNNs solve these problems before taking the concepts and applying them to a vision transformer.


[User] Please transcribe this text

[Assistant] CNNs have been the backbone for vision tests.


[User] Please transcribe this text

[Assistant] Uhm, like, uh, before the advent of, uh, transformers, uh, CNNs have been most widely used, uh, not only for image classification, but, uh, also, uh, segmentation, object detection, style transfer, pose estimation, image synthesis, and other tasks as well.


[User] Please transcribe this text

[Assistant] So what is the fundamental concept here that CNN has that made it so successful at all these tasks, not only at classification but also others?


[User] Please transcribe this text

[Assistant] So uhm, one, uhm, they extract, uh, features from the input image into a dense vector representation. And, uh, the density of, uh, this representation like increases as you, uh, as we go along, uh, the depth of the model.


[User] Please transcribe this text

[Assistant] And, uh, they use, uh, these extracted features to solve the task at hand. Uh, so what do you mean by that? So let's take an example to understand this more clearly.


[User] Please transcribe this text

[Assistant] Or uhm, extracted in a hierarchical manner, like first they are taken as 224 and 224 and to by 64.


[User] Please transcribe this text

[Assistant] And then as you progress along the depth of the texture, though the height and width


[User] Please transcribe this text

[Assistant] Of the, like, uh, representation at a particular level is decreasing. The width of the representation, it becomes dense.


[User] Please transcribe this text

[Assistant] Not shown in this picture is, uh, there is a concept of sliding window or kernel that slides along the input.


[User] Please transcribe this text

[Assistant] Feature density as we, as we go along, uh, along the model step.


[User] Please transcribe this text

[Assistant] And then use some kind of sliding mechanism or shifting mechanism like swing transformer user shifting. So can we take these concepts from CNNs and apply them to the vision transformer to make it better at other computer vision tasks too?


[User] Please transcribe this text

[Assistant] Uh, and the answer is yes, we can. And, uh, this is what is being shown, uh, shown in, uh, spin transformer model in the paper by the others.


[User] Please transcribe this text

[Assistant] And here is a picture of that. And as you can see, the swing transformer model is hierarchical in nature.


[User] Please transcribe this text

[Assistant] Uhm, so on a high level we can say that the input image is divided into some number of non-overlapping windows, uh, and self-advention is calculated only in each window.


[User] Please transcribe this text

[Assistant] And at every next stage, these patches get merged. If we compare it to vanilla vision transformer, there is no hierarchical nature in this.


[User] Please transcribe this text

[Assistant] So uhm, let's take a look at spin transformers architecture.


[User] Please transcribe this text

[Assistant] So this is the core part of the paper which describes how this model works.


[User] Please transcribe this text

[Assistant] Uh, incoming input image of, of size, um, this, uh, h, uh, cross w cross 3, uh, the input, the input image. We flatten, uh,


[User] Please transcribe this text

[Assistant] Uh, this image like we divide this into patches, um, say for a size four by four pixels each, um, and we flatten this four by four pixels into a, into one vector.


[User] Please transcribe this text

[Assistant] And, uh, the non-overlapping patches of size four by four where each pixel has, uh, three values.


[User] Please transcribe this text

[Assistant] For uh, RGB, uh, each patch will be converted into a vector, uh, with length four by four by three, uh, that is 48.


[User] Please transcribe this text

[Assistant] And, uhm, this vector will go through a linear embedding layer, uhm, here. Uhm, sorry, this


[User] Please transcribe this text

[Assistant] Sorry, uh, this vector will go through a linear embedding layer and gets converted to have a dimension C.


[User] Please transcribe this text

[Assistant] So, and this goes through a modified transformer block called swing transformer block that I will show in the next slide. And this has some modifications. After this, we still get a feature map where each element has a dimension C.


[User] Please transcribe this text

[Assistant] Uh, and the transformer and the linear embedding layer, uh, in front of it is called stage one. And from this point on the feature maps, uh, go through several reputations of patch matching and then, uh, Swin transformer blocks. And this procedure will, um, be called a stage.


[User] Please transcribe this text

[Assistant] Uhm, my patch merging is, uh, grouping, uh, two by two adjacent patches by concatenating the four elements and then applying a linear layer.


[User] Please transcribe this text

[Assistant] Uh, for example, the first pass merging since the input, uh,


[User] Please transcribe this text

[Assistant] Uh, feature maps, each portion has a vector size of C, concatenating two by two patches into one, uh, will give a vector of length, uh, four C. Uh, the linear layer following it will, uh, change the channel size from four C to, uh, two C again here.


[User] Please transcribe this text

[Assistant] And, uh, this process, uh, acts as tone sampling resulting the width and height of, uh, um, like feature map, uh, so they get reduced by two. And after patch matching, Swin transformer block is applied.


[User] Please transcribe this text

[Assistant] Uhm, so, uh, as we can see, uh, like patch merging and spin transformer block application, uh, at each level, uh, that's, uh, the reduced path size and then increased, uh, the feature, increased feature representation C2 to C2, C2 to 8C.


[User] Please transcribe this text

[Assistant] As we go along, uh, the model's architecture. And, uh, this is repeated three times and we get four feature maps from different scales. And this is the background structure which can be supplied to object detection, image classification or semantic segmentation.


[User] Please transcribe this text

[Assistant] So, uh, we'll see what, uh, swing transformer block is, uh, uh, and what is so special about the swing, uh, about the swing transformer. Um, like, there are two blocks in, uh, the swing, uh, the swing transformer block.


[User] Please transcribe this text

[Assistant] Uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm,


[User] Please transcribe this text

[Assistant] One is to split the patches into windows and then apply attention only within the patches inside each window.


[User] Please transcribe this text

[Assistant] Uh, this will result, uh, like this will restrict, uh, the transformer complexity from increasing with respect to each feature, maps, patch, access, uh, and, uh, the total number of window size.


[User] Please transcribe this text

[Assistant] So, and the second one is after slicing patches into windows and applying transformers, uh, at the first time, uh, the window splitting will be shifted from, it will be shifted, um, and then, uh, transformer will be applied again.


[User] Please transcribe this text

[Assistant] Like, uh, here after, uh, shifting another transformer, like this block will be applied again.


[User] Please transcribe this text

[Assistant] Uh, this allows, um, like the advantage here is this allows adjacent windows to interact with each other. And then as you go along the depth of the architecture, like each patch will learn to, uh, learn the context of itself and then.


[User] Please transcribe this text

[Assistant] Um, uh, even though attention is only calculated at, at a level, at a stage, attention, uh, though it is calculated at, uh, in a single window, uh, eventually every patch or every pixel will get


[User] Please transcribe this text

[Assistant] And then the third, uh, important thing is while calculating attention def added, uh, relative position bias.


[User] Please transcribe this text

[Assistant] So we'll see what these exactly are. So first we'll talk about shifted window partition. So what is this? So at layerl, if we have like four patches,


[User] Please transcribe this text

[Assistant] Uhm, and after shifting, uh, this will become like this, like a patch will be taken, uh, from here, like, like here.


[User] Please transcribe this text

[Assistant] And then this will result in increased number of patches and then patches will become of different sizes as well.


[User] Please transcribe this text

[Assistant] We know that there is a concept called sliding window in convolutional neural networks where the kernel slides along the image and convolution operation is performed right.


[User] Please transcribe this text

[Assistant] So, uh, but here a simple window based self-attention module, um, like it lacks, um, connections across windows, like, uh, when an attention is calculated in this, uh, window, uh, um, with respect to each, uh, patch will learn its context with respect to other patches in this, in this window only.


[User] Please transcribe this text

[Assistant] Uhm, so to introduce cross window connections, uh, while maintaining efficient computation of, uh,


[User] Please transcribe this text

[Assistant] Uhm, non-overlapping windows. The paper proposes a shifted window concept.


[User] Please transcribe this text

[Assistant] Uh, like partitioning, uh, approach, uh, like which alternates between, uh, two partitioning configurations, uh, in a, like in a consecutive, uh, spin transformer blocks. So here in the layer, uh, L left, uh, so here in this left layer one, layer L, sorry.


[User] Please transcribe this text

[Assistant] A regular window partitioning scheme is adopted and self-attention is computed within each window.


[User] Please transcribe this text

[Assistant] But in the next layer, layer l plus one, the window partitioning is shifted, resulting in new windows.


[User] Please transcribe this text

[Assistant] So here, uhm, the main reason for doing this is, uh, so that, uh, self-computation in the new window crosses the boundaries of previous window layer, uh, in layer L.


[User] Please transcribe this text

[Assistant] Uh, providing, like, uh, connections among, uh, among each window. So, uh, this shifted window partitioning approach introduces connections between, uh,


[User] Please transcribe this text

[Assistant] neighboring non-overlapping windows in the previous layer.


[User] Please transcribe this text

[Assistant] This is found effective based on experiments done in the Swin transformer block. They have tried sliding window as well, but they came to the conclusion based on their experiment results that


[User] Please transcribe this text

[Assistant] Uh, but, uh, we can clearly see that there is an issue here because, uh, in layer L there are four, uh, windows and here there are nine. So, uh, clearly there is a problem here. Uh, let's see how the others, uh, like, uh, like overcome this problem.


[User] Please transcribe this text

[Assistant] Since uhm, they do something called cyclic shift, uh, to uhm,


[User] Please transcribe this text

[Assistant] To merge some of these windows, uh, to make them of same size as the previous layer.


[User] Please transcribe this text

[Assistant] So, ehm, a naive solution is to, ehm, like pad the windows to a size, ehm, like here, ehm, the size of this window is only four by four in this example. And here it is, ehm, sorry, it's two by two and it's, ehm, say four by two and it's, ehm, four by four.


[User] Please transcribe this text

[Assistant] Uh, so a naive solution is to pad the smaller windows to a fixed size, say 4x4 in this case, and mask out the padded values when computing attention. But this results in an increase in number of windows, right?


[User] Please transcribe this text

[Assistant] So, uh, he, uh, if we do that padding thing, uh, in the first layer, we have only four windows. Now we have nine windows resulting in, uh, increased computation of, uh, 2.25 times.


[User] Please transcribe this text

[Assistant] So the other proposed, uh, so the others proposed, uh, a more efficient batch computation approach by


[User] Please transcribe this text

[Assistant] Cyclic shifting, like, uh, here also they have four windows, but what they've done is, uh, they've, uh, shifted some of these patches from, say, uh, the A two by two, uh, patch comes here and, uh, C will be, uh, cyclically shifted here, like B will come here, A will come here and C will come here.


[User] Please transcribe this text

[Assistant] So that, uh, the number of, total number of windows, uh, remain the same, uh, four windows.


[User] Please transcribe this text

[Assistant] So after this shift, uhm, uhm, like, uh, but we have to calculate, uh, attention, right? So for this, there is no need of masking anything because it's the same.


[User] Please transcribe this text

[Assistant] But, uh, here, uh, these patches like, um, uh, come from different, uh, like these belong here and this B comes from here.


[User] Please transcribe this text

[Assistant] So while, uh, doing self, uh, self-attention computation, they mask these, uh, patches. So for computing, say, for example, uh, this patch, like they're, they're masking these, uh, and these, these, uh, patches. And for this, uh, for calculating self-attention in this window, they don't need to mask here.


[User] Please transcribe this text

[Assistant] So, and then, uh, they're doing reverse cyclic shift to, uh, get back, uh, the original, um, like, uh, position of the windows.


[User] Please transcribe this text

[Assistant] So uhm, another thing they have added is relative position bias. Uh, we are, we know from the self-attention paper that, uh, this is how attention is calculated. But here in this bin transform of paper, they have added this term B.


[User] Please transcribe this text

[Assistant] Uh, this is to introduce, uh, uh, relative position, position information to the, like,


[User] Please transcribe this text

[Assistant] Uh, key query product, uh, query key product, uh, before applying softmax to calculate, uh, the value matrix weights.


[User] Please transcribe this text

[Assistant] x or y axis will be somewhere in the range, uh, minus m plus 1 to m plus 1, right? So, uh, to integrate this relative position information, uh, the others prepared a learnable parameter matrix called b hat.


[User] Please transcribe this text

[Assistant] Uh, which shape to 2m-1 and 2m-1 and each element will represent a vector that represents, uh, relative, uh, xy position differences and the actual relative position bias matrix B here, uh, that will be sampled from the B hat which I just mentioned.


[User] Please transcribe this text

[Assistant] And, uh, through experiments, uhm, they found out that without any bias or without, uh,


[User] Please transcribe this text

[Assistant] Uhm, like using absolute position embedding gave the spin transformer an inferior performance. That's why they have added the relative position bias when calculating attention.


[User] Please transcribe this text

[Assistant] Uh, so, um, uh, and, uh, the model size, uh, he's compatible to a vision transformer or data efficient transformer model or, uh, ResNet-50 and ResNet-101. So, uh, the model sizes are, uh, controlled by the size C, uh, here.


[User] Please transcribe this text

[Assistant] That is initial feature map's channel size.


[User] Please transcribe this text

[Assistant] And, uh, they start with, uh, 96 and, um, as the, like, uh, density of the swing transformer increases, they increase the channel size as well.


[User] Please transcribe this text

[Assistant] And, uh, let's study some of the results of, uh, the spin transformer.


[User] Please transcribe this text

[Assistant] And, uh, so compared to, uh, previous state of art transformer based architecture, uh, that is, uh, data efficient transformers, uh, spin transformers noticeably, uh, surpass, uh, the counterpart, uh, architectures with similar complexities.


[User] Please transcribe this text

[Assistant] Uhm, like compared with, uh, state-of-the-art comments, uhm, that is, uh, ResNet, uhm, you know, from 8G16, et cetera.


[User] Please transcribe this text

[Assistant] The Swin transformer achieves better accuracy here as we can see. And even compared to vanilla vision transformer also they achieve improved accuracy.


[User] Please transcribe this text

[Assistant] Uhm, so, uh, so these are all the results, uh, that are done on regular image one, uh, ImageNet 1k models, trained models, and these results are, uh, obtained from, uh, by doing ImageNet 22k pre-trained models.


[User] Please transcribe this text

[Assistant] So we can see the comparison of, uh,


[User] Please transcribe this text

[Assistant] Um, the number of parameters as well. Uh, so for example, in the pre trend model, we can see that, uh, compared to a bit large, uh, that is vanilla transformer with 16 passes, uh, we can see that the number of parameters are less.


[User] Please transcribe this text

[Assistant] Uhm, and even they even consume less number of flops but achieved a similar accuracy on ImageNet.


[User] Please transcribe this text

[Assistant] So uhm, so in the next experiment, this is on object detection, uh, that is Cocoa object detection dataset. And here also we can see that Swing Transformer achieves, uh, like, uh, significantly improved accuracy compared to, uh, like, uh, ResNet 50.


[User] Please transcribe this text

[Assistant] And I, I cross, uh, this, um, various, um, like, like compared to, um, uh, various other models.


[User] Please transcribe this text

[Assistant] And here also Swinlarge, uh, actually, um, has got like achieved, um, better accuracy in this test also and system level comparison also.


[User] Please transcribe this text

[Assistant] And the third experiment they've done is on semantic segmentation on ADE 20k dataset.


[User] Please transcribe this text

[Assistant] Uh, so, um, so we can see that, uh, the, um, model spin laws, uh, get, um, like gets an, uh, improved accuracy score, uh, that is, uh, M-I-O-U score on, um, when compared to, uh, um, data efficient transformer, like we can see it's, um,


[User] Please transcribe this text

[Assistant] Uh, compared to 44, it is 53.5, but at the same time, the model parameters is also 52. Here we can see that it's obviously around, uh, four times more than four times more, uh, and, uh, significantly consumed like has, uh, but more, uh, uh, flops also like more time to time.


[User] Please transcribe this text

[Assistant] So, uh, the others also have done some ablation studies, uh, like without shifting and shifting, uh, with shifting windows and, uh, with, without including, uh, any, um, uh, relative position bias as we have seen in the equation, uh, I mentioned in the previous slide.


[User] Please transcribe this text

[Assistant] And they found that with shifted windows gave better accuracy results on ImageNet classification and this is for object detection and this is for semantic segmentation and across the board.


[User] Please transcribe this text

[Assistant] Shifted windows gave, uh, better accuracy and then using relative position gave better accuracy instead of


[User] Please transcribe this text

[Assistant] Using no, uh, no relative position at all across all, uh, across all tests and all datasets.


[User] Please transcribe this text

[Assistant] And then they've conducted another like conducted another ablation study on, uh,


[User] Please transcribe this text

[Assistant] Uh, the training time taken and the FPS achieved as we can see like the latency significantly decreases when compared to using a sliding window model here.


[User] Please transcribe this text

[Assistant] Whereas here it is in the order of 130 and here it is in 3 and 1.9. It is very less compared to using these other methods.


[User] Please transcribe this text

[Assistant] And finally they have conducted ablation studies on sliding window and shifted window. This is accuracy. Previous one is time taken latency here. And this is the accuracy. And they have measured that shifted window accuracy is more like comparable to sliding window.


[User] Please transcribe this text

[Assistant] Since it has less latency, this model is preferred here in this paper.


[User] Please transcribe this text

[Assistant] So, uh, finally, uh, we have, uh, seen, uh, in, in the spin transform how it produces a hierarchical feature representation.


[User] Please transcribe this text

[Assistant] Uhm, like taking inspiration from convolutional neural networks and it has achieved linear computational complexity with respect to image size because the number of the like we are calculating self-attention only in a particular window not on global scale.


[User] Please transcribe this text

[Assistant] And, uh, we have seen from the experiment results and ablation studies that, uh, the Swin transformer, uh, achieved a state of art performance on, uh, um, uh, uh, POCO, uh, in object detection and ADE 20k semantic segmentation as well. But besides image net, uh, image classification tasks.


[User] Please transcribe this text

[Assistant] So uhm, that's it for my presentation. If you have any questions, please go ahead.


[User] Please transcribe this text

[Assistant] As far as I know, um, transformers have already achieved a state of art on NLP. We can see from the results of, uh, BERT, GPT, uh, BERT models, GPT, two, three models, they're very good at, uh, even giving, uh, like


[User] Please transcribe this text

[Assistant] Displaying jokes as well, and we already saw like in the first week, right? How they're, uhm, like even writing code, learning how to write code in difficult languages like C++ as well, which is like syntax heavy.


[User] Please transcribe this text

[Assistant] Transformer models like have defeated, I can say confidently that they have defeated LSTMs and they're here to stay in MLP. Uhm, and like the only thing that, that is progressing,


[User] Please transcribe this text

[Assistant] Uh, in their model, uh, and they've almost, uh, they've trained entire internet, um, um, entire GitHub pages and everything that's trained. And it's like the complex, as the complexity, um, increases, they are saying that they're like, um, uh, transformer model based, uh, NLP models are.


[User] Please transcribe this text

[Assistant] And then, uh, this week, I think we'll be completing, uh, images and in the coming, uh, coming weeks, I think we'll see exactly, uh, like with results, like where our transformers stand in, uh, natural language processing domain.


[User] Please transcribe this text

[Assistant] Yeah, swing transformer, uh, they've compared to the performer that this is also, um, like this achieve previous to, uh, the swing transformer performer achieve.


[User] Please transcribe this text

[Assistant] If we compare accuracy, uh, shifted window is, uh, almost similar to sliding window.


[User] Please transcribe this text

[Assistant] But if we compare, uh, if we do, if we are like, uh, study this ablation study, um, sliding window is, uh, significantly slower compared to shifting window, uh, when the results are like coming in three milliseconds and one millisecond here.


[User] Please transcribe this text

[Assistant] They are taking like 122 milliseconds. So even though accuracy is similar, uhm, this model is inferior because of the latency present.


[User] Please transcribe this text

[Assistant] Oh, very nice. Can't stop long, I'm afraid, we're very busy.


[User] Please transcribe this text

[Assistant] Kind of remember, I do not allow my premises to be used by drunken bullies.


[User] Please transcribe this text

[Assistant] And every night at 11 on What's Next, you can rely on us.


[User] Please transcribe this text

[Assistant] Seeing, knowing, and understanding Northeast Ohio is something we take very seriously.


[User] Please transcribe this text

[Assistant] Uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh,


[User] Please transcribe this text

[Assistant] I was bi-curious about a local gym. No, I was curious about a local gym and you seemed interested in health and wellness. I was already working out there. You were like, hey, how's that gym? That's right. So I moved to


[User] Please transcribe this text

[Assistant] Yeah, it is. It's cool. And then, so I was looking for like a gym buddy.


[User] Please transcribe this text

[Assistant] No, but like, I, no, I, I think what it was was all joking aside, it was like, I asked you, I was like, how was that gym? And then, you were like, it's cool, I've been going here for a while, it's super cheap. And then we would go and just like, lift weights.


[User] Please transcribe this text

[Assistant] And then I knew you rode and I like, at that time, that's when I like, stopped riding.


[User] Please transcribe this text

[Assistant] Age 10 through 17, and then like I was always a fan, so I always thought it was cool that you were still involved in it also. But I think, uh, I liked, I liked always kind of keeping like, uh, some kind of like one foot in the pond, you know what I mean? Because I started riding a little bit more when we first meeting too, like I got a bike and we were riding, but then like, you know.


[User] Please transcribe this text

[Assistant] Honestly, that was really interesting as a, like, watcher, because, man, it was like pretty crazy, like, how much that shit works. I guess that's kind of a no shit statement, right? But like, it really isn't, like, from the first, dude, it was fucking wild.


[User] Please transcribe this text

[Assistant] And then, like, had some cool- no, I paid for it myself, thank you very much. And then, uh, lived with some roommates, and then we moved to, uh, Woodside, right?


[User] Please transcribe this text

[Assistant] This is a stressful podcast because in a lot of ways there's so much content to go over that like, I feel like we should have like,


[User] Please transcribe this text

[Assistant] Hold on a sec, so, this is the thing though, because when we, when we had a place together, it was you, uh, independent job guy, whatever, fantasy job guy.


[User] Please transcribe this text

[Assistant] No, I was like, I was at my playing weight, which was like, I was big guy, yea, so sport guy, right, big guy, and then I dropped like 35 pounds, and that was, uh, that was like a big changing point because, and I didn't stop drinking at all actually, so.


[User] Please transcribe this text

[Assistant] No, no, it's definitely good, but I mean like, it was more about my diet. I was just eating the same, same thing every night. That's what success is, just like being a psycho and having like super low standards and being like,


[User] Please transcribe this text

[Assistant] Yeah, it was around that time. It was, wanted to do a band, played the drums, didn't want to carry drums, so I was like, I'll just be the singer because I don't have to carry any equipment.


[User] Please transcribe this text

[Assistant] I wanted to play drums. But like, it was, it's just so much easier to be the singer. It still is.


[User] Please transcribe this text

[Assistant] It was perfect and everyone kinda had the same. The word incendiary is about being stubborn.


[User] Please transcribe this text

[Assistant] That's what this band is. It's like, we're gonna make this happen no matter what. We're gonna do this cause it's annoying but like, you do it. Like we were in another interview and I was saying how it's like, basically being like five like old dads, like stubborn dads where it's like,


[User] Please transcribe this text

[Assistant] Yeah, but we needed like, I like people who are like,


[User] Please transcribe this text

[Assistant] Focused and intense for the right reasons, like everyone that we had, like Dan and Rob were like, we're gonna make this happen, we're gonna do it. And like doing annoying shit, being in a band, especially when no one cares about you, is like really annoying. And it's like if you don't think that it's fun and like you don't have the drive to do it,


[User] Please transcribe this text

[Assistant] Everyone's on the same page, like throw their life away and tour full time and that's so sick too. And then we just found people and we had our friends where like we're just gonna like make this work like no matter what.


[User] Please transcribe this text

[Assistant] Uh, under any circumstances, and like, that sort of like, stubbornness, is why, what was it, 10 years, this is our 10th year as a band, it just works.


[User] Please transcribe this text

[Assistant] It's, it's, it's so much more like, I dunno, what do you guys think? I think it's so much more of like,


[User] Please transcribe this text

[Assistant] And it's fun to be on, but it also kinda takes on like a life of its own where it's like, you...


[User] Please transcribe this text

[Assistant] If you could like go back in time and be like, it's 2017, you're gonna be in LA playing a festival, like I would never believe that shit ever, but it's more just, it makes sense with how like focused we were on doing every different thing that came up and we would just agree to anything and just play.


[User] Please transcribe this text

[Assistant] It was like, based on the shit that we liked when we were younger. Just like, kinda like the late 90s, like, victory records.