Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification

TL;DR

This paper introduces DuATM, a dual attention-based framework for person re-identification that learns context-aware feature sequences and performs attentive sequence comparison, improving accuracy over existing single-vector methods.

Contribution

The paper proposes a novel end-to-end trainable dual attention mechanism for context-aware feature sequence learning and attentive comparison in person ReID.

Findings

Outperforms state-of-the-art methods on multiple benchmarks

Effective in handling visual ambiguities in person ReID

Utilizes intra- and inter-sequence attention for feature refinement

Abstract

Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space. However, the methods based on a single feature vector are not sufficient enough to overcome visual ambiguity, which frequently occurs in real scenario. In this paper, we propose a novel end-to-end trainable framework, called Dual ATtention Matching network (DuATM), to learn context-aware feature sequences and perform attentive sequence comparison simultaneously. The core component of our DuATM framework is a dual attention mechanism, in which both intra-sequence and inter-sequence attention strategies are used for feature refinement and feature-pair alignment, respectively. Thus, detailed visual cues contained in the intermediate feature sequences can be automatically exploited and properly compared. We train the proposed DuATM network as a siamese network via a triplet loss assisted with a de-correlation loss and a cross-entropy loss. We conduct extensive experiments on both image and video based ReID benchmark datasets. Experimental results demonstrate the significant advantages of our approach compared to the state-of-the-art methods.