AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification

TL;DR

AD-Cluster introduces an innovative approach for domain adaptive person re-identification by augmenting and discriminatively clustering target domain samples, significantly enhancing model discrimination and outperforming existing methods.

Contribution

The paper proposes a novel augmented discriminative clustering method that leverages iterative density-based clustering and sample augmentation to improve domain adaptive person re-ID.

Findings

Outperforms state-of-the-art methods on Market-1501 and DukeMTMC-reID datasets.

Enhances intra-cluster diversity and discrimination capability of re-ID models.

Achieves large margins of improvement in re-ID accuracy.

Abstract

Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown. Existing methods attempt to address this challenge by transferring image styles or aligning feature distributions across domains, whereas the rich unlabeled samples in target domains are not sufficiently exploited. This paper presents a novel augmented discriminative clustering (AD-Cluster) technique that estimates and augments person clusters in target domains and enforces the discrimination ability of re-ID models with the augmented clusters. AD-Cluster is trained by iterative density-based clustering, adaptive sample augmentation, and discriminative feature learning. It learns an image generator and a feature encoder which aim to maximize the intra-cluster diversity in the sample space and minimize the intra-cluster distance in the feature space in an adversarial min-max manner. Finally, AD-Cluster increases the diversity of sample clusters and improves the discrimination capability of re-ID models greatly. Extensive experiments over Market-1501 and DukeMTMC-reID show that AD-Cluster outperforms the state-of-the-art with large margins.