SANet: Selective Aggregation Network for unsupervised object re-identification

Recent advancements in unsupervised object re-identification have witnessed remarkable progress, which usually focuses on capturing fine-grained semantic information through partitioning or relying on auxiliary networks for optimizing label consistency. However, incorporating extra complex partitioning mechanisms and models leads to non-negligible optimization difficulties, resulting in limited performance gains. To address these problems, this paper presents a Selective Aggregation Network (SANet) to obtain high-quality features and labels for unsupervised object re-identification, which explores primitive fine-grained information of large-scale pre-trained models such as CLIP and designs customized modifications. Specifically, we propose an adaptive selective aggregation module that chooses a set of tokens based on CLIP’s attention scores to aggregate discriminative global features. Built upon the representations output by the adaptive selective aggregation module, we design a dynamic weighted clustering algorithm to obtain accurate confidence-weighted pseudo-class centers for contrastive learning. In addition, a dual confidence judgment strategy is introduced to refine and correct the pseudo-labels by assigning three categories of samples through their noise degree. By this means, the proposed SANet enables discriminative feature extraction and clustering refinement for more precise classification without complex architectures such as feature partitioning or auxiliary models. Extensive experiments on existing standard unsupervised object re-identification benchmarks, including Market1501, MSMT17, and Veri776, demonstrate the effectiveness of the proposed SANet method, and SANet achieves state-of-the-art results over other strong competitors.