Large-scale hyperspectral image (HSI) clustering has become an important research task owing to its promising applications in various fields. Recently, beneficial from the correlation modeling capability of graphs, graph contrastive learning methods have received increasing attention in the clustering task. However, these methods usually have limited ability to explore the high-order correlation as well as beneficial clustering information of large-scale HSI, thus limiting the clustering performance on large-scale HSI. To this end, a novel hypergraph contrastive learning network (HCL-Net) for large-scale HSI clustering is proposed in this paper. Specifically, a diffusion hypergraph-based contrastive clustering mechanism is presented, in which a diffusion hypergraph is constructed to model the high-order correlation in large-scale HSI, thus guiding contrastive learning for obtaining more discriminative representations. Besides, by mining the confident clustering information, a confidence-guided positive-negative updating strategy is designed to dynamically update positives and negatives for contrastive learning, thereby obtaining a more compact clustering structure. The proposed method is evaluated on three public large-scale HSI datasets. The experimental results have demonstrated the superior performance of the proposed HCL-Net over state-of-the-art methods.

The investigation for incomplete multi-modal 3D shape clustering is evolving as a promising task for the field of recognizing massive unlabeled 3D shapes. As two widely adopted 3D shape modalities, point clouds and multiple views not only exhibit rich intra-modal correlations but also encompass complementary structures and appearances of 3D shapes. By effectively modeling the intra-modal and inter-modal correlations, this paper proposes a novel incomplete multi-modal 3D shape clustering method to reveal the underlying clustering associations from incomplete multi-modal 3D shapes. In detail, a similarity-transferred feature prediction module is presented to recover the features of missing instances within one modality with the assistance of similarity exploring from another modality. Then, an intra-to-inter progressive feature fusion module is designed to mine the correlations within the modality as well as between different modalities, thereby obtaining comprehensive 3D shape features for clustering. Extensive experiments on two public 3D shape datasets have demonstrated that the proposed method has achieved promising clustering results under different missing rates.

Few-shot object detection aims to effectively detect novel classes with limited annotated samples. Due to the low-quality instance features obtained by deep learning in data-scarce scenarios, few-shot object detection remains a significant challenge. In this paper, a novel method is proposed to enhance the few-shot object detection performance by focusing on the generalizability and discriminability of instance features. In detail, by synthesizing auxiliary instances that embed diverse attributes, a mask-guided instance feature generation module is presented to alleviate the overfitting on sample-specific characteristics, thereby facilitating the acquisition of generalizable object-relevant knowledge. Then, to learn discriminative object-relevant attributes, a hybrid cross-layer and intra-layer contrastive learning mechanism is designed to enhance the discriminability of instance features by building contrastive constraints between instances within and across layers. Experimental results on two widely used benchmarks demonstrate the effectiveness of the proposed method.

Unsupervised 3D shape clustering is emerging as a promising research topic in multimedia and computer vision field. Considering the flexibility of acquiring multiple views for 3D shapes, this paper proposes a contrastive multi-view learning network (CMVL-Net) to cluster unlabeled 3D shapes from multiple views. To the best of our knowledge, this is the first multi-view-oriented 3D shape deep clustering method. The key to this method lies in how to capture highly discriminative 3D shape features suitable for clustering. By exploring consistency and complementarity among multiple views, a cross-view contrastive clustering mechanism is proposed to learn clustering-specified discriminative 3D shape features. To obtain a more compact 3D shape clustering structure, a consensus graph-guided contrastive constraint is designed to encourage cluster-wise consistency learning under the guidance of potential category associations among shapes. Experimental results on two widely used benchmark datasets demonstrate the effectiveness of the proposed method.

Cross-domain person re-identification (Re-ID) is a challenging and important task in monitoring safety and procedure compliance of industrial work places. In this article, a novel method is proposed to reduce background induced domain shift for adaptive person Re-ID. Specifically, a foreground-background joint clustering module is proposed to extract discriminative foreground and background features and an attention-based feature disentanglement module is designed to reduce the interference of background with the extraction of discriminative foreground features. Experimental results on three widely used person Re-ID benchmarking datasets (Market-1501, DukeMTMC-reID, and MSMT17) have demonstrated that the proposed method achieves promising performance compared with the state-of-the-art methods.