Progressive Context-Aware Graph Feature Learning for Target Re-Identification

Abstract

This paper aims at robust and discriminative feature learning for target re-identification (Re-ID). In addition to paying attention to the individual appearance information as in most Re-ID methods, we further utilize the abundant contextual information as additional clues to guide the feature learning. Graph as a format of structured data is used to represent the target sample with its context. It describes the first-order appearance information of the samples and the second-order topological relationship information among samples, based on which we compute the feature representation by learning a graph feature embedding. We provide a detailed analysis of graph convolutional network mechanism applied in target Re-ID and propose a novel progressive context-aware graph feature learning method, in which the message passing is dominated by a pre-defined adjacency relationship followed by a learned relationship in a self-adaptive way. The proposed method fully exploits and utilizes contextual information at a low cost for Re-ID. Extensive experiments on five Re-ID benchmarks demonstrate the state-of-the-art performance of the proposed method.