Learning Disentangled Representation for Robust Person Re-identification

TL;DR

This paper introduces IS-GAN, a novel generative adversarial network that disentangles identity-related and unrelated features for robust person re-identification, improving accuracy without auxiliary supervision.

Contribution

The paper proposes a new GAN-based method for disentangling features in person reID, enabling more robust identification without extra labels.

Findings

Outperforms state-of-the-art on Market-1501, CUHK03, DukeMTMC-reID datasets

Effectively disentangles identity and other factors without auxiliary info

Improves robustness to intra-class variations

Abstract

We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons can have the same attribute and the same person's appearance looks different with viewpoint changes. Recent reID methods focus on learning discriminative features but robust to only a particular factor of variations (e.g., human pose), which requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to disentangle identity-related and -unrelated features from person images. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose, scale changes). To this end, we introduce a new generative adversarial network, dubbed \emph{identity shuffle GAN} (IS-GAN), that factorizes these features using identification labels without any auxiliary information. We also propose an identity-shuffling technique to regularize the disentangled features. Experimental results demonstrate the effectiveness of IS-GAN, significantly outperforming the state of the art on standard reID benchmarks including the Market-1501, CUHK03 and DukeMTMC-reID. Our code and models are available online: https://cvlab-yonsei.github.io/projects/ISGAN/.