用于细粒度视频动作识别的判别分段聚焦网络

IF 5.2 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Multimedia Computing Communications and Applications Pub Date : 2024-03-26 DOI:10.1145/3654671
Baoli Sun, Xinchen Ye, Tiantian Yan, Zhihui Wang, Haojie Li, Zhiyong Wang
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引用次数: 0

摘要

细粒度视频动作识别的目的是识别细类动作之间的微小差异和区别性差异。虽然最近提出了许多动作识别方法,以更好地模拟时空表征,但如何模拟具有区分性的原子动作之间的相互作用,从而有效地描述类间和类内的变化却被忽视了,而这对于理解细粒度动作至关重要。在这项工作中,我们设计了一种判别片段聚焦网络(Discriminative Segment Focus Network,DSFNet)来挖掘片段相关性的可判别性,并定位与判别动作相关的片段,从而实现细粒度视频动作识别。首先,我们提出了分层相关性推理(HCR)模块,该模块在多个时间尺度上明确建立不同片段之间的相关性,并通过利用与其他片段的相关性来增强每个片段。其次,设计了一个分辨片段聚焦(DSF)模块,通过一致性约束强制给定片段的可分辨性和分类置信度之间的一致性,从 HCR 的增强表征中定位出与行动最相关的片段。最后,这些本地化的片段表示与整个视频的全局动作表示相结合,以提高最终识别率。在两个细粒度动作识别数据集(即 FineGym 和 Diving48)和两个动作识别数据集(即 Kinetics400 和 Something-Something)上的大量实验结果表明,与最先进的方法相比,我们的方法非常有效。
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Discriminative Segment Focus Network for Fine-grained Video Action Recognition

Fine-grained video action recognition aims to identify minor and discriminative variations among fine categories of actions. While many recent action recognition methods have been proposed to better model spatio-temporal representations, how to model the interactions among discriminative atomic actions to effectively characterize inter-class and intra-class variations has been neglected, which is vital for understanding fine-grained actions. In this work, we devise a Discriminative Segment Focus Network (DSFNet) to mine the discriminability of segment correlations and localize discriminative action-relevant segments for fine-grained video action recognition. Firstly, we propose a hierarchic correlation reasoning (HCR) module which explicitly establishes correlations between different segments at multiple temporal scales and enhances each segment by exploiting the correlations with other segments. Secondly, a discriminative segment focus (DSF) module is devised to localize the most action-relevant segments from the enhanced representations of HCR by enforcing the consistency between the discriminability and the classification confidence of a given segment with a consistency constraint. Finally, these localized segment representations are combined with the global action representation of the whole video for boosting final recognition. Extensive experimental results on two fine-grained action recognition datasets, i.e., FineGym and Diving48, and two action recognition datasets, i.e., Kinetics400 and Something-Something, demonstrate the effectiveness of our approach compared with the state-of-the-art methods.

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来源期刊
CiteScore
8.50
自引率
5.90%
发文量
285
审稿时长
7.5 months
期刊介绍: The ACM Transactions on Multimedia Computing, Communications, and Applications is the flagship publication of the ACM Special Interest Group in Multimedia (SIGMM). It is soliciting paper submissions on all aspects of multimedia. Papers on single media (for instance, audio, video, animation) and their processing are also welcome. TOMM is a peer-reviewed, archival journal, available in both print form and digital form. The Journal is published quarterly; with roughly 7 23-page articles in each issue. In addition, all Special Issues are published online-only to ensure a timely publication. The transactions consists primarily of research papers. This is an archival journal and it is intended that the papers will have lasting importance and value over time. In general, papers whose primary focus is on particular multimedia products or the current state of the industry will not be included.
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