IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID (2108.02413v1)

Abstract: Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the labeled source domain's knowledge to improve the model's discriminability on the unlabeled target domain. From a novel perspective, we argue that the bridging between the source and target domains can be utilized to tackle the UDA re-ID task, and we focus on explicitly modeling appropriate intermediate domains to characterize this bridging. Specifically, we propose an Intermediate Domain Module (IDM) to generate intermediate domains' representations on-the-fly by mixing the source and target domains' hidden representations using two domain factors. Based on the "shortest geodesic path" definition, i.e., the intermediate domains along the shortest geodesic path between the two extreme domains can play a better bridging role, we propose two properties that these intermediate domains should satisfy. To ensure these two properties to better characterize appropriate intermediate domains, we enforce the bridge losses on intermediate domains' prediction space and feature space, and enforce a diversity loss on the two domain factors. The bridge losses aim at guiding the distribution of appropriate intermediate domains to keep the right distance to the source and target domains. The diversity loss serves as a regularization to prevent the generated intermediate domains from being over-fitting to either of the source and target domains. Our proposed method outperforms the state-of-the-arts by a large margin in all the common UDA re-ID tasks, and the mAP gain is up to 7.7% on the challenging MSMT17 benchmark. Code is available at https://github.com/SikaStar/IDM.

• The paper proposes an Intermediate Domain Module (IDM) to explicitly model intermediate domains for improved unsupervised domain adaptive person re-identification.
• IDM uses dynamic mixing of source and target representations guided by domain factors and geodesic paths, regulated by bridge and diversity losses.
• Experiments show IDM achieves significant performance gains, including up to 7.7% mAP on the challenging MSMT17 benchmark, surpassing state-of-the-art methods.

An Analysis of "IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID"

The paper presents an approach to unsupervised domain adaptive person re-identification (UDA re-ID), which leverages an Intermediate Domain Module (IDM) to bridge the gap between labeled source and unlabeled target domains. This method stands out by concentrating on the explicit modeling of intermediate domains, thereby enhancing the knowledge transfer from the source to the target domain in situations where there are extreme distribution differences and no overlap in label spaces.

Summary and Methodology

Fundamentally, the authors propose that the integration of appropriate intermediate domains can significantly ameliorate the UDA re-ID process. The IDM facilitates this by creating intermediate representations through the dynamic mixing of hidden representations from the source and target domains. The mixing is directed by two domain factors, balancing the representations based on their proximity to source or target domains, following the shortest geodesic path in a manifold space. This geodesic path ensures that the intermediate domains maintain optimal distances to the reference domains, providing a smoother adaptation pathway and potentially improving the transferability of learned features.

To complement this process, the paper introduces two critical losses:

1. Bridge Losses: Applied to prediction and feature spaces, these losses ensure that intermediate domains remain appropriately spaced between source and target domains.
2. Diversity Loss: Acts as a regularization mechanism preventing intermediate domains from overfitting to any one domain, thus maintaining diversity in representation.

The authors demonstrate the effectiveness of their approach through extensive experiments across common UDA re-ID tasks, where IDM achieves notable improvements. The results indicate a maximal mean average precision (mAP) gain of up to 7.7% on the challenging MSMT17 benchmark, outperforming state-of-the-art approaches.

Implications

From a practical standpoint, IDM introduces a novel, adaptable component that can integrate into existing models with minimal overhead. This enhancement can provide substantial performance improvements, which are critical for real-world applications of re-ID systems, particularly in security and surveillance where domain shifts are prevalent. The theoretical implications are equally significant. The explicit use of intermediate domains to bridge disparate datasets suggests a broader potential to improve domain adaptation techniques across various machine learning applications.

Future Directions

The work sets a foundation for future research into intermediate domain modeling in unsupervised adaptation contexts. Exploring the application of IDM across different domains could validate its generality. Furthermore, eviscerating the manifold structure, particularly in high-dimensional spaces characteristic of modern deep learning models, might offer deeper insights into optimizing domain adaptation processes with intermediate representations.

Given the robust numerical results and the innovative approach towards handling domain discrepancies through intermediate domains, the IDM module represents a significant contribution to the existing UDA methodologies, promising further avenues for enhancing domain adaptability in AI systems.