Adversarial Multi-scale Feature Learning for Person Re-identification

TL;DR

This paper introduces a novel multi-scale feature learning framework with gradient regularization for person re-identification, achieving state-of-the-art results with minimal additional computation.

Contribution

It proposes a combined multi-scale feature learning approach with gradient regularization to improve discriminative feature extraction in Person ReID.

Findings

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

Effective multi-scale feature fusion and regularization.

Minimal increase in test-time computation.

Abstract

Person Re-identification (Person ReID) is an important topic in intelligent surveillance and computer vision. It aims to accurately measure visual similarities between person images for determining whether two images correspond to the same person. The key to accurately measure visual similarities is learning discriminative features, which not only captures clues from different spatial scales, but also jointly inferences on multiple scales, with the ability to determine reliability and ID-relativity of each clue. To achieve these goals, we propose to improve Person ReID system performance from two perspective: \textbf{1).} Multi-scale feature learning (MSFL), which consists of Cross-scale information propagation (CSIP) and Multi-scale feature fusion (MSFF), to dynamically fuse features cross different scales.\textbf{2).} Multi-scale gradient regularizor (MSGR), to emphasize ID-related factors and ignore irrelevant factors in an adversarial manner. Combining MSFL and MSGR, our method achieves the state-of-the-art performance on four commonly used person-ReID datasets with neglectable test-time computation overhead.