scape Artist, a podcast for the culturally curious traveller. Each week travel journalist and photographer Edwina Hart interviews a special guest who has mastered the art of escape. The series explores how travel has influenced their lives and creative endeavours.
Edwina is joined by an exciting line-up of artists, tastemakers and travel insiders whose love affair with travel will transport you to enchanting, far-flung corners of the globe.
Covering everything from hidden gems to bourgeoning hotspots, as well as some wanderlust-worthy places to add to your wish list. This podcast is pure escapism for those always dreaming of their next destination.
Who is Edwina Hart?
Edwina Hart is an Australian travel writer and photographer who regularly contributes to titles such as Vogue Australia/Vogue.com.au, Lonely Planet, ESCAPE, The Telegraph UK, The New Zealand Herald, International Traveller magazine, Delicious. magazine, Get Lost magazine, Vacations and Travel, and more.
Edwina's work has seen her crafting stories all over the world and travelling on assignment to every single continent including Antarctica.
Her photography has been featured in both Australian and international newspapers, such as The Sydney Morning Herald, The Age, The Sunday Telegraph, The Telegraph UK and The Guardian UK.
Edwina is a member of the Australian Society of Travel Writers (ASTW). In 2013, she lived as a writer-in-residence at the legendary Left Bank bookstore, Shakespeare and Company. Edwina was awarded the World Nomad's international 'Passport & Plate' food and travel writing scholarship in 2016.
Season 1's inspiring line-up of jetsetters will be announced soon. Join us as we uncover the incredible travel adventures of an eclectic mix of creatives. Stay tuned!\section{Notations}
	
	
	






	
		
		
		

		
		



		
		
	
	
	
		

		
\begin{abstract}
We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/ending frames that can be used as positive examples for model training. Most conventional approaches directly train a binary classifier using such imbalance data, thus achieving inferior results. The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy.  Specifically, we design a novel dense regression network (DRN) to regress the distances from each frame to the starting (ending) frame of the video segment described by the query.  We also propose a simple but effective IoU regression head module to explicitly consider the localization quality of the grounding results (i.e., the IoU between the predicted location and the ground truth). Experimental results show that our approach significantly outperforms state-of-the-arts on three datasets (i.e., Charades-STA, ActivityNet-Captions, and TACoS). 
\end{abstract}

	\section{Introduction}
	
	Video grounding is an important yet challenging task in computer vision, which requires the machine to watch a video and localize the starting and ending time of the target video segment that corresponds to the given query, as shown in Figure~\ref{fig:Brief}. This task has drawn increasing attention over the past few years due to its vast potential applications in video understanding ~\cite{wang2016temporal, carreira2017quo,zeng2019graph,zeng2019breaking,chen2019relation}, video retrieval ~\cite{yu2018a, dong2019dual}, and human-computer interaction~\cite{singha2018dynamic,huang2020graph,zhu2019auxrn}, etc.
   
   
   

 The task, however, is very challenging due to several reasons: 1) It is nontrivial to build connections between the query and complex video contents; 2) Localizing actions of interest precisely in a video with complex backgrounds is very difficult. More critically, a video can often contain many thousands of frames, but it  may have only a few annotated starting/ending frames (namely the positive training examples), making the problem even more challenging.
    Previous approaches often adopt a two-stage pipeline~\cite{gao2017tall, xu2019multilevel, ge2019mac}, where they generate the proposals and rank them according to their similarities with the query. However, this pipeline incurs two issues: 1) One video often contains thousands of proposals, resulting in a heavy computation cost when comparing proposal-query pairs.
	2) The performance highly relies on the quality of proposals. 
	\begin{figure}[!t]
		\centering
		\includegraphics[width=\linewidth]{Figures/Fig1.pdf}
	
		\caption{An illustrative example of the video grounding task. Given a video and a query, the video grounding task aims to identify the starting and ending time of the video segment described by the query. One key challenge  of this task is how to leverage dense supervision upon sparsely annotated starting and ending frames.}
		\label{fig:Brief}
		\vspace{-0.15in}
	\end{figure}
	To address the above issues, one-stage video grounding methods~\cite{chen2019localizing,yuan2019to,ghosh2019excl} have been studied. Yuan~\emph{et al}\onedot~\cite{yuan2019to} propose to learn a representation of the video-query pair and use a multi-layer perceptron (MLP) to regress the starting and ending time. 
	Chen~\emph{et al}\onedot~\cite{chen2019localizing} and Ghosh~\emph{et al}\onedot~\cite{ghosh2019excl} attempt to predict two probabilities at each frame, which indicate whether this frame is a starting (or ending) frame of the target video segment. 
	The grounding result is obtained by selecting the frame with the largest starting (or ending) probability.
	However, the existing two-stage and one-stage methods have one common issue: they neglect the rich information from the frames within the ground truth.
	
	Recently, anchor-free approaches~\cite{huang2015densebox, redmon2016you, tian2019fcos, law2018cornernet, kong2019foveabox} for one-stage object detection become increasingly popular because of their simplicity and effectiveness. In this vein, Tian~\emph{et al}\onedot~\cite{tian2019fcos} propose the FCOS framework to solve object detection in a per-pixel prediction fashion. Specifically, FCOS trains a regression network to directly predict the distance from each pixel in the object to the object's boundary. 
	This idea is helpful for video grounding. If we train a model to predict the distance from each frame to the ground truth boundary, then all the frames within the ground truth can be leveraged as positive training samples. In this way, the number of positive samples is sufficiently increased and thus benefits the training. 
	
	

	
	In this paper, we propose a dense regression network for video grounding, which consists of four modules, including a video-query interaction module, a location regression head, a semantic matching head, and an IoU regression head.
	The main idea is as straightforward as training a regression module to directly regress the ground truth boundary from each frame within the ground truth.
	In the training, all frames within the ground truth are selected as positive samples. 
	By doing so, the sparse annotation is able to be used to generate more positive training samples sufficiently, which boosts grounding performance eventually.
	
	For each video-query pair, our model produces dense predictions (\emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot, one predicted temporal bounding box for each frame) while we are only interested in the one that matches the query best. To select the best grounding result, we focus on two perspectives: 1) Does the box match the query semantically? 2) Does the box match the temporal boundary of the ground truth? Specifically, we train a semantic matching head to predict a score for each box, which indicates whether the content in the box matches the query semantically. However, this score cannot directly reflect the localization quality (\emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot, the IoU with the ground truth), which is of vital importance for video grounding. This motivates us to further consider the localization quality of each prediction. To do so, one may use the ``centerness" assumption in FCOS, which, however, is empirically found inapplicable for video grounding (see Table~\ref{tab:iou}).
	In this paper, we train an IoU regression head to directly estimate the IoU between the predicted box and the ground truth. Last, we combine the matching score and the IoU score to find the best grounding result.
	It is worth noting that the dense regression network works in a one-stage manner.

	We evaluate our proposed method on three popular benchmarks for video grounding, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot, Charades-STA~\cite{gao2017tall}, ActivityNet-Captions~\cite{krishna2017dense} and TACoS ~\cite{regneri2013grounding}.
	

	
	To sum up, our contributions are as follows:
	\begin{itemize}
		\item We propose a