Trunk-branch Contrastive Network with Multi-view Deformable Aggregation for Multi-view Action Recognition

TL;DR

This paper introduces TBCNet, a novel multi-view action recognition network that combines multi-view feature fusion with contrastive learning to improve the recognition accuracy by capturing detailed local features.

Contribution

The paper proposes a trunk-branch contrastive network with multi-view deformable aggregation and a weighted contrastive loss, enhancing multi-view feature fusion and detail extraction for action recognition.

Findings

Achieves state-of-the-art performance on four datasets.

Effectively captures intra- and cross-view spatial-temporal correlations.

Improves recognition accuracy over existing RGB-based methods.

Abstract

Multi-view action recognition aims to identify actions in a given multi-view scene. Traditional studies initially extracted refined features from each view, followed by implemented paired interaction and integration, but they potentially overlooked the critical local features in each view. When observing objects from multiple perspectives, individuals typically form a comprehensive impression and subsequently fill in specific details. Drawing inspiration from this cognitive process, we propose a novel trunk-branch contrastive network (TBCNet) for RGB-based multi-view action recognition. Distinctively, TBCNet first obtains fused features in the trunk block and then implicitly supplements vital details provided by the branch block via contrastive learning, generating a more informative and comprehensive action representation. Within this framework, we construct two core components: the multi-view deformable aggregation and the trunk-branch contrastive learning. MVDA employed in the trunk block effectively facilitates multi-view feature fusion and adaptive cross-view spatio-temporal correlation, where a global aggregation module is utilized to emphasize significant spatial information and a composite relative position bias is designed to capture the intra- and cross-view relative positions. Moreover, a trunk-branch contrastive loss is constructed between aggregated features and refined details from each view. By incorporating two distinct weights for positive and negative samples, a weighted trunk-branch contrastive loss is proposed to extract valuable information and emphasize subtle inter-class differences. The effectiveness of TBCNet is verified by extensive experiments on four datasets including NTU-RGB+D 60, NTU-RGB+D 120, PKU-MMD, and N-UCLA dataset. Compared to other RGB-based methods, our approach achieves state-of-the-art performance in cross-subject and cross-setting protocols.