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HERO: HiErarchical spatio-tempoRal reasOning with Contrastive Action Correspondence for End-to-End Video Object Grounding

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Fei WuAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 3801 - 3810

https://doi.org/10.1145/3503161.3548333

Published: 10 October 2022 Publication History

Abstract

<u>V</u>ideo <u>O</u>bject <u>G</u>rounding (VOG) is the problem of associating spatial object regions in the video to a descriptive natural language query. This is a challenging vision-language task that necessitates constructing the correct cross-modal correspondence and modeling the appropriate spatio-temporal context of the query video and caption, thereby localizing the specific objects accurately. In this paper, we tackle this task by a novel framework called <u>H</u>i<u>E</u>rarchical spatio-tempo<u>R</u>al reas<u>O</u>ning (HERO) with contrastive action correspondence. We study the VOG task at two aspects that prior works overlooked: (1) Contrastive Action Correspondence-aware Retrieval. Notice that the fine-grained video semantics (e.g., multiple actions) is not totally aligned with the annotated language query (e.g., single action), we first introduce the weakly-supervised contrastive learning that classifies the video as action-consistent and action-independent frames relying on the video-caption action semantic correspondence. Such a design can build the fine-grained cross-modal correspondence for more accurate subsequent VOG. (2) Hierarchical Spatio-temporal Modeling Improvement. While transformer-based VOG models present their potential in sequential modality (i.e., video and caption) modeling, existing evidence also indicates that the transformer suffers from the issue of the insensitive spatio-temporal locality. Motivated by that, we carefully design the hierarchical reasoning layers to decouple fully connected multi-head attention and remove the redundant interfering correlations. Furthermore, our proposed pyramid and shifted alignment mechanisms are effective to improve the cross-modal information utilization of neighborhood spatial regions and temporal frames. We conducted extensive experiments to show our HERO outperforms existing techniques by achieving significant improvement on two benchmark datasets.

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cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

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Published: 10 October 2022

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Key R & D Projects of the Ministry of Science and Technology
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MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

Lisboa, Portugal

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Cited By

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https://dl.acm.org/doi/10.1145/3664647.3681632
Li MHan KXu JLi YWu TZhao ZMiao JZhang SChen JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Cross-modal Observation Hypothesis InferenceProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681591(466-475)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681591
Li LFang JXue J(2024)Cognitive Traffic Accident AnticipationIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2024.337846016:5(17-32)Online publication date: Sep-2024
https://doi.org/10.1109/MITS.2024.3378460
Wang JMa ZCao DLe YXiao JChua TEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Deconfounded Multimodal Learning for Spatio-temporal Video GroundingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613822(7521-7529)Online publication date: 26-Oct-2023
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Yao XGao JChen MXu CEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Video Entailment via Reaching a Structure-Aware Cross-modal ConsensusProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612345(4240-4249)Online publication date: 26-Oct-2023
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https://dl.acm.org/doi/10.1145/3581783.3612314
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