Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID

Abstract: Domain adaptive object re-ID aims to transfer the learned knowledge from the labeled source domain to the unlabeled target domain to tackle the open-class re-identification problems. Although state-of-the-art pseudo-label-based methods have achieved great success, they did not make full use of all valuable information because of the domain gap and unsatisfying clustering performance. To solve these problems, we propose a novel self-paced contrastive learning framework with hybrid memory. The hybrid memory dynamically generates source-domain class-level, target-domain cluster-level and un-clustered instance-level supervisory signals for learning feature representations. Different from the conventional contrastive learning strategy, the proposed framework jointly distinguishes source-domain classes, and target-domain clusters and un-clustered instances. Most importantly, the proposed self-paced method gradually creates more reliable clusters to refine the hybrid memory and learning targets, and is shown to be the key to our outstanding performance. Our method outperforms state-of-the-arts on multiple domain adaptation tasks of object re-ID and even boosts the performance on the source domain without any extra annotations. Our generalized version on unsupervised object re-ID surpasses state-of-the-art algorithms by considerable 16.7% and 7.9% on Market-1501 and MSMT17 benchmarks.

• The paper presents a unified framework that leverages hybrid memory and a self-paced learning strategy to integrate source and target domain information.
• It employs a hybrid memory structure to manage class-, cluster-, and instance-level signals, achieving mAP improvements of up to 16.7% on benchmarks.
• The self-paced mechanism iteratively refines clustering by filtering unreliable clusters, thereby enhancing training stability and model robustness.

Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID

This paper presents a novel approach to domain adaptive object re-identification (re-ID), specifically addressing the challenges of transferring knowledge from a labeled source domain to an unlabeled target domain. The proposed method, named Self-paced Contrastive Learning with Hybrid Memory, introduces a robust framework designed to handle the complexities of unsupervised domain adaptation (UDA).

Framework Overview

The primary contribution of this paper lies in the development of a unified contrastive learning framework that dynamically incorporates information from both the source and target domains. The hybrid memory constructs class-level, cluster-level, and instance-level supervisory signals, thereby effectively addressing the limitations of previous pseudo-label-based methods.

Key elements of the framework include:

• Hybrid Memory: This memory structure simultaneously manages and updates varying levels of information, thus enhancing the model's ability to learn discriminative feature representations across domains. It dynamically adjusts to reflect source-domain classes, target-domain clusters, and un-clustered instances.
• Self-paced Strategy: The approach iteratively refines clustering through a self-paced mechanism, which filters out less reliable clusters progressively. This strategy ensures training stability and improves the representational quality over time.

Numerical Results

Empirical results demonstrate that this method significantly outperforms existing state-of-the-art techniques. Noteworthy improvements are achieved in mean Average Precision (mAP), with uplift ranges up to 16.7% and 7.9% on the Market-1501 and MSMT17 benchmarks, respectively. These results underscore the framework's efficiency in utilizing both domain knowledge and potential insights from outliers which previous methods discarded or overlooked.

Theoretical and Practical Implications

The methodological advancements presented open new avenues for research in domain adaptation and unsupervised learning. The framework's ability to leverage all available data, including outliers and less confident clusters, promises enhanced performance in broader applications of re-ID beyond merely person or vehicle identification.

Theoretically, the self-paced learning strategy provides a new perspective on handling noisy pseudo-labels and capitalizing on clustering reliability. This has the potential to be extended beyond the re-ID task to other applications requiring fine-grained classification under domain shifts.

Future Prospects in AI

Looking ahead, the integration of such frameworks into real-world systems could improve smart city infrastructures, enhance surveillance architectures, and even accelerate developments in autonomous systems. As the methodology matures, its applicability might expand to areas demanding robust domain adaptation solutions, including medical imaging and cross-domain language processing.

In summary, the presented method marks a significant step forward in the field of unsupervised domain adaptive object re-identification, offering a comprehensive solution to existing challenges through an innovative self-paced, contrastive learning framework with hybrid memory. The strong performance metrics and theoretical soundness suggest promising future developments in artificial intelligence and machine learning.