Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Straightforward Framework For Video Retrieval Using CLIP   Video Retrieval is a challenging task where a text query is matched to a
video or vice versa. Most of the existing approaches for addressing such a
problem rely on annotations made by the users. Although simple, this approach
is not always feasible in practice. In this work, we explore the application of
the language-image model, CLIP, to obtain video representations without the
need for said annotations. This model was explicitly trained to learn a common
space where images and text can be compared. Using various techniques described
in this document, we extended its application to videos, obtaining
state-of-the-art results on the MSR-VTT and MSVD benchmarks.

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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int