Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-Modal Interaction Networks for Query-Based Moment Retrieval in
  Videos   Query-based moment retrieval aims to localize the most relevant moment in an
untrimmed video according to the given natural language query. Existing works
often only focus on one aspect of this emerging task, such as the query
representation learning, video context modeling or multi-modal fusion, thus
fail to develop a comprehensive system for further performance improvement. In
this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to
consider multiple crucial factors for this challenging task, including (1) the
syntactic structure of natural language queries; (2) long-range semantic
dependencies in video context and (3) the sufficient cross-modal interaction.
Specifically, we devise a syntactic GCN to leverage the syntactic structure of
queries for fine-grained representation learning, propose a multi-head
self-attention to capture long-range semantic dependencies from video context,
and next employ a multi-stage cross-modal interaction to explore the potential
relations of video and query contents. The extensive experiments demonstrate
the effectiveness of our proposed method.

33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: hep-th