Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect

TL;DR

This paper introduces a novel temporal fusion framework for video-based person re-identification that considers semantic differences and frame relationships, significantly improving accuracy.

Contribution

It proposes a multi-stage semantic fusion network and an inter-frame attention module to enhance feature richness and reduce redundancy in video ReID.

Findings

Achieves state-of-the-art re-identification accuracy.

Effectively reduces information loss and redundancy.

Improves feature aggregation across semantic levels and temporal relationships.

Abstract

Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video. However, existing video-based ReID methods do not consider the semantic difference brought by the outputs of different network stages, which potentially compromises the information richness of the person features. Furthermore, traditional methods ignore important relationship among frames, which causes information redundancy in fusion along the time axis. To address these issues, we propose a novel general temporal fusion framework to aggregate frame features on both semantic aspect and time aspect. As for the semantic aspect, a multi-stage fusion network is explored to fuse richer frame features at multiple semantic levels, which can effectively reduce the information loss caused by the traditional single-stage fusion. While, for the time axis, the existing intra-frame attention method is improved by adding a novel inter-frame attention module, which effectively reduces the information redundancy in temporal fusion by taking the relationship among frames into consideration. The experimental results show that our approach can effectively improve the video-based re-identification accuracy, achieving the state-of-the-art performance.