Learning to Adapt Invariance in Memory for Person Re-identification

TL;DR

This paper introduces a novel unsupervised domain adaptation framework for person re-identification that leverages invariance properties, a memory module, and a graph-based neighbor prediction to improve accuracy across different datasets.

Contribution

It proposes a new invariance-based adaptation framework with a memory module and GPP method, advancing the state-of-the-art in person re-ID domain adaptation.

Findings

Invariance properties are essential for effective adaptation.

Memory enhances invariance learning with minimal extra cost.

GPP significantly boosts re-ID accuracy.

Abstract

This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain. Existing methods are primary to reduce the inter-domain shift between the domains, which however usually overlook the relations among target samples. This paper investigates into the intra-domain variations of the target domain and proposes a novel adaptation framework w.r.t. three types of underlying invariance, i.e., Exemplar-Invariance, Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar memory is introduced to store features of samples, which can effectively and efficiently enforce the invariance constraints over the global dataset. We further present the Graph-based Positive Prediction (GPP) method to explore reliable neighbors for the target domain, which is built upon the memory and is trained on the source samples. Experiments demonstrate that 1) the three invariance properties are indispensable for effective domain adaptation, 2) the memory plays a key role in implementing invariance learning and improves the performance with limited extra computation cost, 3) GPP could facilitate the invariance learning and thus significantly improves the results, and 4) our approach produces new state-of-the-art adaptation accuracy on three re-ID large-scale benchmarks.