Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Relevance-based Margin for Contrastively-trained Video Retrieval Models   Video retrieval using natural language queries has attracted increasing
interest due to its relevance in real-world applications, from intelligent
access in private media galleries to web-scale video search. Learning the
cross-similarity of video and text in a joint embedding space is the dominant
approach. To do so, a contrastive loss is usually employed because it organizes
the embedding space by putting similar items close and dissimilar items far.
This framework leads to competitive recall rates, as they solely focus on the
rank of the groundtruth items. Yet, assessing the quality of the ranking list
is of utmost importance when considering intelligent retrieval systems, since
multiple items may share similar semantics, hence a high relevance. Moreover,
the aforementioned framework uses a fixed margin to separate similar and
dissimilar items, treating all non-groundtruth items as equally irrelevant. In
this paper we propose to use a variable margin: we argue that varying the
margin used during training based on how much relevant an item is to a given
query, i.e. a relevance-based margin, easily improves the quality of the
ranking lists measured through nDCG and mAP. We demonstrate the advantages of
our technique using different models on EPIC-Kitchens-100 and YouCook2. We show
that even if we carefully tuned the fixed margin, our technique (which does not
have the margin as a hyper-parameter) would still achieve better performance.
Finally, extensive ablation studies and qualitative analysis support the
robustness of our approach. Code will be released at
\url{https://github.com/aranciokov/RelevanceMargin-ICMR22}.

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The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org