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Contrastive Perturbation Network for Weakly Supervised Temporal Sentence Grounding

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14425))

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Abstract

The purpose of temporal sentence grounding is to find the most relevant temporal period corresponding to the natural language query in an unmodified video. In recent years, the weak supervision paradigm, which does not require tedious annotations of starting and ending positions of the corresponding video segments, has gained significant attention due to its low annotation cost and reasonable efficiency. However, its effectiveness is seriously affected by the low-quality negative samples generated with random strategies. In this paper, we propose a Contrastive Perturbation Network (CPN), which introduces perturbation schemes into contrastive learning of weak supervised temporal sentence grounding. The perturbation involves both the proposal generation module and the reconstruction module of the CPN. In the proposal generation module, we introduce the KL divergence loss to minimize the distribution differences between the perturbed positive and real positive proposals, to force the network to be robust to the redundant information and learn fine-grained alignments between the text and video modalities. The reconstruction module leverages the perturbed features to generate a highly challenging negative proposal and strengthens the supervision to the proposal generation module by distinguishing the positive and negative proposals with the use of contrastive learning. Extensive experiments on two public benchmarks, i.e., ActivityNet Captions and Charades-STA, demonstrate that the proposed CPN could effectively improve the performance of weakly supervised temporal sentence grounding.

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Acknowledgments

This work is partly supported by the Zhejiang Province Natural Science Foundation of China (No. LQ21F020014 and No. LZ23F020007) and the National Natural Science Foundation of China (No. 62002091).

Author information

Authors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Tingting Han, Yuanxin Lv, Zhou Yu, Jun Yu & Jianping Fan

  2. China Academy of Electronics and Information Technology of CETC, Beijing, China

    Liu Yuan

Authors
  1. Tingting Han
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  2. Yuanxin Lv
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  3. Zhou Yu
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  4. Jun Yu
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  5. Jianping Fan
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  6. Liu Yuan
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Corresponding author

Correspondence to Tingting Han .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Han, T., Lv, Y., Yu, Z., Yu, J., Fan, J., Yuan, L. (2024). Contrastive Perturbation Network for Weakly Supervised Temporal Sentence Grounding. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14425. Springer, Singapore. https://doi.org/10.1007/978-981-99-8429-9_36

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  • DOI: https://doi.org/10.1007/978-981-99-8429-9_36

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  • Print ISBN: 978-981-99-8428-2

  • Online ISBN: 978-981-99-8429-9

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