File size: 9,507 Bytes
ebcfe72
dbbf2c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
<html>

<head lang="en">
    <meta charset="UTF-8">
    <meta http-equiv="x-ua-compatible" content="ie=edge">

    <title>Affective VisDial</title>

    <meta name="description" content="">

    <meta name="viewport" content="width=device-width, initial-scale=1">

    <link rel="apple-touch-icon" href="apple-touch-icon.png">

    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css">
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.8.0/codemirror.min.css">
    <link rel="stylesheet" href="assets/css/app.css">

    <link rel="stylesheet" href="assets/css/bootstrap.min.css">

    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.8.0/codemirror.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.5.3/clipboard.min.js"></script>
    <script src="js/app.js"></script>
</head>

<body>
    <div class="container">
        <div class="row">
            <h2 class="col-md-12 text-center">
                Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning
                Based on Visually Grounded Conversations</br>
		<small></small>
            </h2>
        </div>

        <!--- Authors List --->
        <div class="row">
            <div class="col-md-12 text-center">
                <ul class="list-inline">
                    <li>
                        <a href="https://kilichbek.github.io/webpage/">
                            Kilichbek Haydarov
                        </a>
                        </br>KAUST
                    </li>
                    <li>
                        <a href="https://xiaoqian-shen.github.io/">
                            Xiaoqian Shen
                        </a>
                        </br>KAUST
                    </li>
                    <li>
                        <a href="https://avinashsai.github.io/">
                            Avinash Madasu
                        </a>
                        </br>KAUST
                    </li>
                    <li>
                        <a href="#">
                            Mahmoud Salem
                        </a>
                        </br>KAUST
                    </li>
                </br>
                    <li>
                        <a href="https://healthunity.org/team/jia-li/">
                            Jia Li
                        </a>
                        </br>Stanford University, HealthUnity
                    </li>
                    <li>
                        <a href="https://research.google/people/GamaleldinFathyElsayed/">
                            Gamaleldin Elsayed
                        </a>
                        </br>Google DeepMind
                    </li>
                    <li>
                        <a href="https://www.mohamed-elhoseiny.com/">
                            Mohamed Elhoseiny
                        </a>
                        </br>KAUST
                    </li>
                </ul>
            </div>
        </div>
        
        <!--- Teaser ---->
        <div class="row" id="header_img"> 
            <figure class="col&#45;md&#45;4 col&#45;md&#45;offset&#45;4"> 
            <image src="assets/img/web_teaser.png" class="img&#45;responsive" alt="overview"> 
                <figcaption> 
                </figcaption> 
            </figure> 
        </div> 
        <!--- Links --->
        <div class="row">
            <div class="col-md-6 col-md-offset-3">
                <h3>
                <!-- <h3 class="text&#45;center"> -->
                    Links
                </h3>
                <div class="col-md-6 col-md-offset-3 text-center">
                    <ul class="nav nav-pills nav-justified">
                        <li>
                            <a href="https://arxiv.org/abs/2308.16349">
                                Paper
                            </a>
                        </li>
                        <li>
                            <a href="#">
                                Dataset (coming soon)
                            </a>
                        </li>
                        <li>
                            <a href="https://github.com/Vision-CAIR/affectiveVisDial">
                                Code
                            </a>
                        </li>
                        <!---
                        <li>
                            <a href="img/modsine.txt">
                                BibTeX
                            </a>
                        </li>
                        --->    
			            <li>
                            <a href="mailto:[email protected]">
                                Contact
                            </a>
                        </li>
                    </ul>
                </div>
            </div>
        </div>

        <!--- End of Links --->

        <!--- Abstract --->
        <div class="row">
            <div class="col-md-6 col-md-offset-3">
                <h3>
                    Overview
                </h3>
                <p class="text-justify">
                    We introduce Affective Visual Dialog, an emotion explanation 
                    and reasoning task as a testbed for research on understanding
                     the formation of emotions in visually-grounded
                    conversations. The task involves three skills: 
                    (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction 
                    and (3) Affective emotion explanation generation
                    based on the dialog. Our key contribution is the collection of a 
                    large-scale dataset, dubbed AffectVisDial, consisting of 50K 
                    10-turn visually grounded dialogs as well as
                    concluding emotion attributions and dialog-informed textual emotion 
                    explanations, resulting in a total of 27,180
                    working hours. We explain our design decisions in collecting the 
                    dataset and introduce the questioner and answerer tasks that are 
                    associated with the participants in the
                    conversation. We train and demonstrate solid Affective Visual Dialog 
                    baselines adapted from state-of-the-art models. Remarkably, 
                    the responses generated by our models show promising emotional 
                    reasoning abilities in response to visually grounded conversations
                </p>
            </div>
        </div>

        <!--- Data Collection Process--->
        <!--- Abstract --->
        <div class="row">
            <div class="col-md-6 col-md-offset-3">
                <h3>
                    Data Collection Process
                </h3>
                <!-- 16:9 aspect ratio -->
                <div class="embed-responsive embed-responsive-16by9">
                    <iframe  class="embed-responsive-item" src="https://drive.google.com/file/d/10BGIvpQH_4tkXl_QVZJf5bNQtKXhakmo/preview"  allow="autoplay"></iframe>
                </div>
            </div>
        </div>
        
        <div class="row">
            <div class="col-md-6 col-md-offset-3">
                <h3>
                    Qualitative Results
                </h3>

				 <div id="header_img"> 
					<figure class="figure">
                         <image src="assets/img/dialog_based_qa.png" class="img&#45;responsive" alt="dialog_task"> 
						 <figcaption class="figure-caption text-center"> 
                            Qualitative Examples of Dialog-Based Question Answering Task. Open the image in new tab for better view.
							
						 </figcaption> 
					</figure>
				 </div>
                 
                 <figure class="figure">
                    <image src="assets/img/qual_examples.png" class="img&#45;responsive" alt="explanation_task"> 
                    <figcaption class="figure-caption text-center"> 
                        Qualitative Examples of Emotion Explanation Generation Task. Open the image in new tab for better view.
                    </figcaption> 
               </figure>

            </div>
        </div>
        <div class="row">
            <div class="col-md-6 col-md-offset-3">
                <h3>
                    Acknowledgements
                </h3>
                <p class="text-justify">
                    This project is funded by KAUST
                    BAS/1/1685-01-01, SDAIA-KAUST Center of Excellence
                    in Data Science and Artificial Intelligence. The authors express 
                    their appreciation to Jack Urbanek, Sirojiddin Karimov, and Umid Nejmatullayev 
                    for their valuable assistance in data collection setup. Lastly, the authors extend their
                    gratitude to the diligent efforts of the Amazon Mechanical
                    Turkers, DeepenAI, and SmartOne teams, as their contributions were indispensable for the successful completion of
                    this work.
                </p>
            </div>
        </div>
    </div>
</body>
</html>