Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification (1811.10144v3)

Abstract: Domain adaptation in person re-identification (re-ID) has always been a challenging task. In this work, we explore how to harness the natural similar characteristics existing in the samples from the target domain for learning to conduct person re-ID in an unsupervised manner. Concretely, we propose a Self-similarity Grouping (SSG) approach, which exploits the potential similarity (from global body to local parts) of unlabeled samples to automatically build multiple clusters from different views. These independent clusters are then assigned with labels, which serve as the pseudo identities to supervise the training process. We repeatedly and alternatively conduct such a grouping and training process until the model is stable. Despite the apparent simplify, our SSG outperforms the state-of-the-arts by more than 4.6% (DukeMTMC to Market1501) and 4.4% (Market1501 to DukeMTMC) in mAP, respectively. Upon our SSG, we further introduce a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting (i.e. the number of independent identities from the target domain is unknown). Without spending much effort on labeling, our SSG ++ can further promote the mAP upon SSG by 10.7% and 6.9%, respectively. Our Code is available at: https://github.com/OasisYang/SSG .

• The paper presents SSG, a method that exploits inherent similarities in unlabeled target samples to create clusters for iterative unsupervised training in person re-identification.
• It utilizes both global body and local part cues to generate pseudo identities, enhancing clustering accuracy and feature discrimination.
• The enhanced SSG++ approach achieves up to 10.7% mAP improvement, narrowing the performance gap with supervised methods in cross-domain adaptation.

Self-similarity Grouping: A Novel Strategy for Unsupervised Domain Adaptation in Person Re-Identification

The paper "Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification" presents a methodological advancement in the challenging domain of unsupervised domain adaptation (UDA) for person re-identification (re-ID). The paper introduces a Self-similarity Grouping (SSG) technique that exploits inherent similarities in unlabeled samples from the target domain to enhance re-ID performance.

Key Methodological Insights

The authors propose the SSG method that harnesses the structural features within target domain samples to create clusters using various cues. These cues range from global body information to localized parts, allowing the creation of multiple independent clusters from different perspectives. Each cluster is assigned a pseudo identity to supervise the training process. The clustering and training are executed iteratively until the model stabilizes.

The SSG method displayed significant improvements over existing state-of-the-art methods, achieving gains of 4.6% in mAP on DukeMTMC → Market1501 and 4.4% on Market1501 → DukeMTMC benchmarks. This reflects SSG's efficacy in narrowing the performance gap between unsupervised and supervised methods.

Beyond basic SSG, the authors introduce SSG++, a semi-supervised approach for one-shot domain adaptation in open-set conditions (where the target domain's identities are unknown). This approach leverages clustering-guided annotation, sampling images strategically based on dense groupings to reduce the potential misidentification of identities.

In numerical terms, SSG++ pushes mAP enhancements of 10.7% on Market1501 → DukeMTMC and 6.9% on DukeMTMC → Market1501, highlighting SSG++'s capacity to approach fully supervised performance levels without exhaustive manual labeling.

Theoretical and Practical Implications

The paper's UDA strategy addresses notable challenges posed by the domain-specific biases inherent in transfer learning for person re-ID. By systematically exploring both global and local features, the SSG approach ensures a robust and discriminative representation that transcends individual dataset discrepancies.

For theoretical implications, this research extends the understanding of unsupervised learning mechanisms, particularly in clustering dynamics and pseudo-labeling efficacy. By utilizing domain-inherent characteristics for clustering, the method not only broadens the applicability of person re-ID models across divergent datasets but also signals new directions for unsupervised adaptation in other complex visual recognition tasks.

Practically, SSG and its augmented version, SSG++, offer industry practitioners a streamlined method to deploy robust re-ID systems across diverse surveillance datasets, alleviating the need for extensive labeling efforts typically associated with domain-specific supervised models.

Future Scope and Developments

Moving forward, the research opens avenues for exploring more granular clustering techniques and the integration of advanced neural architectures that could further enhance feature extraction and representation. Additionally, investigating the application of SSG in conjunction with real-time adaptive learning systems could further solidify the technique's application in dynamic environments.

Moreover, extending this approach to other forms of cross-domain adaptation tasks, such as object detection and fine-grained classification, would be a valuable exploration. The principles of SSG, when applied to these domains, might yield significant advancements in handling unlabeled datasets across varying contexts.

In conclusion, this paper contributes a valuable perspective to unsupervised domain adaptation, particularly in its novel strategy towards utilizing inherent dataset properties for pseudo-supervised learning. The methodological rigor, combined with practical usability, marks an essential contribution to the future of adaptive visual recognition systems.