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    <abstract>The task of spoken video grounding aims to localize moments in videos that are relevant to descriptive spoken queries. However, extracting semantic information from speech and modeling the cross-modal correlation pose two critical challenges. Previous studies solve them by representing spoken queries based on the matched video frames, which require tremendous effort for frame-level labeling. In this work, we investigate weakly-supervised spoken video grounding, i.e., learning to localize moments without expensive temporal annotations. To effectively represent the cross-modal semantics, we propose Semantic Interaction Learning (SIL), a novel framework consisting of the acoustic-semantic pre-training (ASP) and acoustic-visual contrastive learning (AVCL). In ASP, we pre-train an effective encoder for the grounding task with three comprehensive tasks, where the robustness task enhances stability by explicitly capturing the invariance between time- and frequency-domain features, the conciseness task avoids over-smooth attention by compressing long sequence into segments, and the semantic task improves spoken language understanding by modeling the precise semantics. In AVCL, we mine pseudo labels with discriminative sampling strategies and directly strengthen the interaction between speech and video by maximizing their mutual information. Extensive experiments demonstrate the effectiveness and superiority of our method.</abstract>
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%0 Conference Proceedings
%T Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning
%A Wang, Ye
%A Lin, Wang
%A Zhang, Shengyu
%A Jin, Tao
%A Li, Linjun
%A Cheng, Xize
%A Zhao, Zhou
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wang-etal-2023-weakly
%X The task of spoken video grounding aims to localize moments in videos that are relevant to descriptive spoken queries. However, extracting semantic information from speech and modeling the cross-modal correlation pose two critical challenges. Previous studies solve them by representing spoken queries based on the matched video frames, which require tremendous effort for frame-level labeling. In this work, we investigate weakly-supervised spoken video grounding, i.e., learning to localize moments without expensive temporal annotations. To effectively represent the cross-modal semantics, we propose Semantic Interaction Learning (SIL), a novel framework consisting of the acoustic-semantic pre-training (ASP) and acoustic-visual contrastive learning (AVCL). In ASP, we pre-train an effective encoder for the grounding task with three comprehensive tasks, where the robustness task enhances stability by explicitly capturing the invariance between time- and frequency-domain features, the conciseness task avoids over-smooth attention by compressing long sequence into segments, and the semantic task improves spoken language understanding by modeling the precise semantics. In AVCL, we mine pseudo labels with discriminative sampling strategies and directly strengthen the interaction between speech and video by maximizing their mutual information. Extensive experiments demonstrate the effectiveness and superiority of our method.
%R 10.18653/v1/2023.acl-long.611
%U https://aclanthology.org/2023.acl-long.611
%U https://doi.org/10.18653/v1/2023.acl-long.611
%P 10914-10932

Markdown (Informal)

[Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning](https://aclanthology.org/2023.acl-long.611) (Wang et al., ACL 2023)

• Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (Wang et al., ACL 2023)

ACL

• Ye Wang, Wang Lin, Shengyu Zhang, Tao Jin, Linjun Li, Xize Cheng, and Zhou Zhao. 2023. Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10914–10932, Toronto, Canada. Association for Computational Linguistics.