Few-shot industrial defect segmentation (FIDS) is an extremely challenging task in industrial inspection, which focuses on segmenting unseen defect categories with only a few samples. Existing few-shot segmentation methods are commonly developed with constrained feature extraction capabilities, making it difficult to accurately segment diverse unseen defects from distinctive industrial scenarios. To this end, we aim to leverage strong generalization strengths of the large-scale visual foundation model, segment anything model (SAM), to handle FIDS task. However, simply incorporating SAM into FIDS is insufficient for automatically categorizing defects, as the SAM heavily relies on manual prompts for segmentation. To address this, we propose a novel reference-defect prompt learning method RDPrompter, which learns adaptive prompts from defect similarities based on SAM for guiding automated FIDS. Specifically, to obtain adaptive prompts, we introduce a multiscale feature extraction strategy to mine rich defect features by the image encoder of SAM. Then, self-correcting probability prototype and cosine similarity attention are proposed to form a mixed similarity aggregation module, which obtains multiscale feature similarities for defect localization. Based on the similarities, a prompt embedding construction module is designed to further extract fine defect location information and generate prompt embeddings for FIDS. Sufficient experiments on MVTec-Unseen, SDD, FSSD-12 and the collected CID datasets demonstrate the superiority of our method.

Vessel re-identification (ReID) serves as a foundational task for intelligent maritime transportation systems. To enhance maritime surveillance capabilities, this study investigates video-based vessel ReID, a critical yet underexplored task in intelligent transportation systems. The lack of relevant datasets has limited the progress of Video-based vessel ReID research work. We established ViV-ReID, the first publicly available large-scale video-based vessel ReID dataset, comprising 480 vessel identities captured from 20 cross-port camera views (7,165 tracklets and 1.14 million frames), establishing a benchmark for advancing vessel ReID from image to video processing. Videos offer significantly richer information than single-frame images. The dynamic nature of video often leads to fragmented spatio-temporal features causing disrupted contextual understanding, and to address this problem, we further propose a Bidirectional Structural-Aware Spatial-Temporal Graph Network (Bi-SSTN) that explicitly aligns spatio-temporal features using vessel structural priors. Extensive experiments on the ViV-ReID dataset demonstrate that image-based ReID methods often show suboptimal performance when applied to video data. Meanwhile, it is crucial to validate the effectiveness of spatio-temporal information and establish performance benchmarks for different methods. The Bidirectional Structural-Aware Spatial-Temporal Graph Network (Bi-SSTN) significantly outperforms state-of-the-art methods on ViV-ReID, confirming its efficacy in modeling vessel-specific spatio-temporal patterns. Project web page: https://vsislab.github.io/ViV_ReID/.

Graph Neural Networks (GNNs) have gained much more attention in the representation learning for the graph-structured data. However, the labels are always limited in the graph, which easily leads to the overfitting problem and causes the poor performance. To solve this problem, we propose a new framework called IGCN, short for Informative Graph Convolutional Network, where the objective of IGCN is designed to obtain the informative embeddings via discarding the task-irrelevant information of the graph data based on the mutual information. As the mutual information for irregular data is intractable to compute, our framework is optimized via a surrogate objective, where two terms are derived to approximate the original objective. For the former term, it demonstrates that the mutual information between the learned embeddings and the ground truth should be high, where we utilize the semi-supervised classification loss and the prototype based supervised contrastive learning loss for optimizing it. For the latter term, it requires that the mutual information between the learned node embeddings and the initial embeddings should be high and we propose to minimize the reconstruction loss between them to achieve the goal of maximizing the latter term from the feature level and the layer level, which contains the graph encoder-decoder module and a novel architecture GCN$_{Info}$. Moreover, we provably show that the designed GCN$_{Info}$can better alleviate the information loss and preserve as much useful information of the initial embeddings as possible. Experimental results show that the IGCN outperforms the state-of-the-art methods on 7 popular datasets.

Multi-view clustering aims to learn discriminative representations from multi-view data. Although existing methods show impressive performance by leveraging contrastive learning to tackle the representation gap between every two views, they share the common limitation of not performing semantic alignment from a global perspective, resulting in the undermining of semantic patterns in multi-view data. This paper presents CSOT, namely Common Semantics via Optimal Transport, to boost contrastive multi-view clustering via semantic learning in a common space that integrates all views. Through optimal transport, the samples in multiple views are mapped to the joint clusters which represent the multi-view semantic patterns in the common space. With the semantic assignment derived from the optimal transport plan, we design a semantic learning module where the soft assignment vector works as a global supervision to enforce the model to learn consistent semantics among all views. Moreover, we propose a semantic-aware re-weighting strategy to treat samples differently according to their semantic significance, which improves the effectiveness of cross-view contrastive representation learning. Extensive experimental results demonstrate that CSOT achieves the state-of-the-art clustering performance.

To improve reconstruction performance in imagery compressive sensing, the present paper changes solving a block image compressive sensing reconstruction into a convex optimization problem. First, a Total-Variation norm minimization constraints model that includes both L1 and L2 norm functions is established. The split Bregman iterative method solves the model with convex optimization. Then, a robust adaptive image block compressive sensing algorithm is studied based on an analysis of the image features. The image is divided into blocks, and an overlap image block compressive reconstruction method is proposed. Finally, to solve the block effect caused by block compressive sensing reconstruction, a novel image overlap block compressive sensing reconstruction based on the Poisson function is suggested to avoid the block effect in the reconstruction process. The experimental results show that compared with other traditional compressive sensing reconstruction algorithms, the proposed method can generate a better image reconstruction result. According to the PSNR evaluation, when the sampling rate is 0.3, the proposed method is improved by more than 20.98% compared to the conventional techniques, and according to the SSIM evaluation, it has improved by more than 11.92% from the traditional methods. We can also find that the proposed method has better construction effect for traffic sign image recognition compared with ordinary natural image reconstruction. When the sampling rate is only 0.1, the PSNR value reaches 44.28dB, and the SSIM reconstruction accuracy reaches 98.14%. After reconstructing different types and characteristic images, it is supported that the proposed algorithm has good robustness and anti-noise performance.

Recently, the source-free domain adaptation (SFDA) problem has attracted much attention, where the pre-trained model for the source domain is adapted to the target domain in the absence of source data. However, due to domain shift, the negative alignment usually exists between samples from the same class, which may lower intra-class feature similarity. To address this issue, we present a self-supervised representation learning strategy for SFDA, named as neighborhood-aware mutual information (NAMI), which maximizes the mutual information (MI) between the representations of target samples and their corresponding neighbors. Moreover, we theoretically demonstrate that NAMI can be decomposed into a weighted sum of local MI, which suggests that the weighted terms can better estimate NAMI. To this end, we introduce neighborhood consensus score over the set of weakly and strongly augmented views and point-wise density based on neighborhood, both of which determine the weights of local MI for NAMI by leveraging the neighborhood information of samples. The proposed method can significantly handle domain shift and adaptively reduce the noise in the neighborhood of each target sample. In combination with the consistency loss over views, NAMI leads to consistent improvement over existing state-of-the-art methods on three popular SFDA benchmarks.

Recently, functional brain network has been used for the classification of brain disorders, such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods either ignore the non-imaging information associated with the subjects and the relationship between the subjects, or cannot identify and analyze disease-related local brain regions and biomarkers, leading to inaccurate classification results. This paper proposes a local-to-global graph neural network (LG-GNN) to address this issue. A local ROI-GNN is designed to learn feature embeddings of local brain regions and identify biomarkers, and a global Subject-GNN is then established to learn the relationship between the subjects with the embeddings generated by the local ROI-GNN and the non-imaging information. The local ROI-GNN contains a self-attention based pooling module to preserve the embeddings most important for the classification. The global Subject-GNN contains an adaptive weight aggregation block to generate the multi-scale feature embedding corresponding to each subject. The proposed LG-GNN is thoroughly validated using two public datasets for ASD and AD classification. The experimental results demonstrated that it achieves the state-of-the-art performance in terms of various evaluation metrics.

Recently, methods for unsupervised embedding learning have exhibited promising results for extracting desirable representations from unlabeled samples. In general, most methods learn the feature embeddings by handling each sample individually while the structural and semantic relationships between samples are not fully exploited. As a result, the learned embeddings are not sufficiently discriminative. To make use of such inter-sample information for deep embedding learning, this paper proposes an unsupervised method based on the graph convolutional network (GCN). On one hand, our method encodes structural information between the samples corresponding to the nodes in a local neighbourhood of the GCN graph. On the other hand, it leverages the mutual information between the original samples and the augmented ones to ensure that they are globally consistent with each other. Extensive experiments show that our method is not just robust to augmentation perturbations, but also learns discriminative embeddings. Consequently, it achieves the state-of-the-art performance on several challenging datasets.