Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification (2007.10315v1)

Abstract: Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge domain gaps. Recently, there has been a growing interest in using unsupervised domain adaptation to address this scalability issue. Existing methods typically conduct adaptation on the representation space that contains both id-related and id-unrelated factors, thus inevitably undermining the adaptation efficacy of id-related features. In this paper, we seek to improve adaptation by purifying the representation space to be adapted. To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively. Our model involves a disentangling module that encodes cross-domain images into a shared appearance space and two separate structure spaces, and an adaptation module that performs adversarial alignment and self-training on the shared appearance space. The two modules are co-designed to be mutually beneficial. Extensive experiments demonstrate that the proposed joint learning framework outperforms the state-of-the-art methods by clear margins.

• The paper proposes a novel joint learning framework that disentangles identity-related features and adapts them specifically to improve cross-domain person re-identification performance.
• The framework demonstrates superior performance, achieving significant improvements (e.g., up to 24.5% mAP) over state-of-the-art methods on benchmark cross-domain re-identification tasks.
• The disentangling approach creates realistic synthetic images for better domain adaptation and suggests potential applications in related areas like image translation and style transfer.

Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification

The paper "Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification" addresses a significant challenge within the domain of person re-identification (re-id), primarily focusing on the application of unsupervised domain adaptation (UDA) methods to tackle the scalability issue arising from domain gaps. While supervised re-id models have shown substantial progress, their deployment in varying domains typically leads to performance degradation due to diverse factors such as changes in season, background, or camera perspective. This paper presents a novel framework that manages these issues by prudently disentangling id-related and id-unrelated features and focusing adaptation efforts solely on the id-related feature space.

The authors introduce an innovative joint learning framework that bridges the gap between two essential tasks: disentanglement and adaptation. By disentangling the feature representation into id-related and id-unrelated components, the subsequent adaptation algorithm can operate exclusively within the purified id-related space, thereby enhancing the efficacy of UDA. This construction involves a disentangling module and an adaptation module, which collaboratively leverage shared appearance encodings across cross-domain images and adopt adversarial alignment and self-training processes to strengthen intra-class similarity and inter-class difference.

The experimental results corroborate the robustness and effectiveness of this joint learning framework. Notably, the proposed method outstrips state-of-the-art techniques by considerable margins on several benchmark datasets, demonstrating superior performance on cross-domain adaptation tasks. For instance, the framework shows an improvement of up to 24.5% in mAP and 16.8% in Rank@1 over competing methodologies in challenging tasks such as Market to Duke and Duke to MSMT.

The crucial element of this framework lies in its unique ability to disentangle representation into distinct appearance and structure spaces. Through cross-domain cycle-consistent image generation, the disentangled model manages to create realistic synthetic images that maintain the id features across different styles. This not only supports better domain adaptation but also suggests potential applications in other areas where feature disentanglement and domain adaptation are critical, such as image translation and style transfer.

The implications of this research are noteworthy. The joint learning framework could serve as a foundational structure for future cross-domain re-id solutions, enhancing model generalization in diverse environments without extensive labeling efforts. The increased performance in this setting is indicative of a promising direction for reducing domain-specific biases and enhancing model adaptability across varying contexts.

In conclusion, the paper contributes a well-defined and experimentally validated approach to improving cross-domain person re-identification by focusing adaptation within a disentangled id-related feature space. The methods and insights provided by this work have substantial implications for furthering research in unsupervised domain adaptation, potentially catalyzing new developments in AI with effective solutions to the pervasive challenges of domain variation and feature disentanglement.