Multi-scale 2D Representation Learning for weakly-supervised moment retrieval Article Swipe

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Li Ding , Rui Wu , Yongqiang Tang , Zhizhong Zhang , Wensheng Zhang ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2111.02741

Video moment retrieval aims to search the moment most relevant to a given language query. However, most existing methods in this community often require temporal boundary annotations which are expensive and time-consuming to label. Hence weakly supervised methods have been put forward recently by only using coarse video-level label. Despite effectiveness, these methods usually process moment candidates independently, while ignoring a critical issue that the natural temporal dependencies between candidates in different temporal scales. To cope with this issue, we propose a Multi-scale 2D Representation Learning method for weakly supervised video moment retrieval. Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates. Two dimensions in this map indicate the start and end time points of these candidates. Then, we select top-K candidates from each scale-varied map with a learnable convolutional neural network. With a newly designed Moments Evaluation Module, we obtain the alignment scores of the selected candidates. At last, the similarity between captions and language query is served as supervision for further training the candidates' selector. Experiments on two benchmark datasets Charades-STA and ActivityNet Captions demonstrate that our approach achieves superior performance to state-of-the-art results.

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Computer Science

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Artificial Intelligence

Convolutional Neural Network

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Computer science Moment (physics) Similarity (geometry) Construct (python library) Scale (ratio) Benchmark (surveying) Artificial intelligence Convolutional neural network Representation (politics) Process (computing) Pattern recognition (psychology) Machine learning Image (mathematics) Classical mechanics Physics Political science Politics Geography Geodesy Operating system Programming language Quantum mechanics Law

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2111.02741
PDF: https://arxiv.org/pdf/2111.02741
OA Status: green
References: 26
Related Works: 10
OpenAlex ID: https://openalex.org/W3161289369

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DOI: https://doi.org/10.48550/arxiv.2111.02741

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Title: Multi-scale 2D Representation Learning for weakly-supervised moment retrieval

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Language: en

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Publication year: 2021

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Publication date: 2021-11-04

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Authors: Li Ding, Rui Wu, Yongqiang Tang, Zhizhong Zhang, Wensheng Zhang

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Landing page: https://arxiv.org/abs/2111.02741

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Concepts: Computer science, Moment (physics), Similarity (geometry), Construct (python library), Scale (ratio), Benchmark (surveying), Artificial intelligence, Convolutional neural network, Representation (politics), Process (computing), Pattern recognition (psychology), Machine learning, Image (mathematics), Classical mechanics, Physics, Political science, Politics, Geography, Geodesy, Operating system, Programming language, Quantum mechanics, Law

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abstract_inverted_index.require	23
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abstract_inverted_index.usually	53
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abstract_inverted_index.Learning	85
abstract_inverted_index.achieves	189
abstract_inverted_index.approach	188
abstract_inverted_index.boundary	25
abstract_inverted_index.captions	162
abstract_inverted_index.critical	61
abstract_inverted_index.datasets	180
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abstract_inverted_index.existing	17
abstract_inverted_index.ignoring	59
abstract_inverted_index.indicate	116
abstract_inverted_index.language	13, 164
abstract_inverted_index.network.	140
abstract_inverted_index.recently	42
abstract_inverted_index.relevant	9
abstract_inverted_index.results.	194
abstract_inverted_index.selected	155
abstract_inverted_index.superior	190
abstract_inverted_index.temporal	24, 66, 72, 102, 107
abstract_inverted_index.training	172
abstract_inverted_index.alignment	151
abstract_inverted_index.benchmark	179
abstract_inverted_index.community	21
abstract_inverted_index.construct	96
abstract_inverted_index.different	71
abstract_inverted_index.expensive	29
abstract_inverted_index.learnable	137
abstract_inverted_index.retrieval	2
abstract_inverted_index.selector.	175
abstract_inverted_index.Evaluation	146
abstract_inverted_index.candidates	56, 69, 130
abstract_inverted_index.dimensions	112
abstract_inverted_index.retrieval.	92
abstract_inverted_index.similarity	160
abstract_inverted_index.supervised	36, 89
abstract_inverted_index.ActivityNet	183
abstract_inverted_index.Experiments	176
abstract_inverted_index.Multi-scale	82
abstract_inverted_index.annotations	26
abstract_inverted_index.candidates'	174
abstract_inverted_index.candidates.	110, 125, 156
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abstract_inverted_index.supervision	169
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abstract_inverted_index.Charades-STA	181
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abstract_inverted_index.convolutional	138
abstract_inverted_index.Representation	84
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cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
sustainable_development_goals[0].id	https://metadata.un.org/sdg/4
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citation_normalized_percentile