Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Read, Watch, and Move: Reinforcement Learning for Temporally Grounding
  Natural Language Descriptions in Videos   The task of video grounding, which temporally localizes a natural language
description in a video, plays an important role in understanding videos.
Existing studies have adopted strategies of sliding window over the entire
video or exhaustively ranking all possible clip-sentence pairs in a
pre-segmented video, which inevitably suffer from exhaustively enumerated
candidates. To alleviate this problem, we formulate this task as a problem of
sequential decision making by learning an agent which regulates the temporal
grounding boundaries progressively based on its policy. Specifically, we
propose a reinforcement learning based framework improved by multi-task
learning and it shows steady performance gains by considering additional
supervised boundary information during training. Our proposed framework
achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset
and Charades-STA dataset while observing only 10 or less clips per video.

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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: eess