Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification
This addresses the problem of matching persons across video clips for surveillance or security applications, representing an incremental improvement with a novel method for feature aggregation.
The paper tackles video-based person re-identification by proposing MG-RAFA, an attentive feature aggregation module that uses multi-granularity references to aggregate spatio-temporal features, achieving state-of-the-art performance on three benchmark datasets.
Video-based person re-identification (reID) aims at matching the same person across video clips. It is a challenging task due to the existence of redundancy among frames, newly revealed appearance, occlusion, and motion blurs. In this paper, we propose an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation. In order to determine the contribution/importance of a spatial-temporal feature node, we propose to learn the attention from a global view with convolutional operations. Specifically, we stack its relations, i.e., pairwise correlations with respect to a representative set of reference feature nodes (S-RFNs) that represents global video information, together with the feature itself to infer the attention. Moreover, to exploit the semantics of different levels, we propose to learn multi-granularity attentions based on the relations captured at different granularities. Extensive ablation studies demonstrate the effectiveness of our attentive feature aggregation module MG-RAFA. Our framework achieves the state-of-the-art performance on three benchmark datasets.