Zero-shot anomaly detection (ZSAD) is a challenging task that aims to detect anomalies in images without any prior knowledge of the anomaly classes. This task is especially difficult because anomalies are rare, diverse, and often manifest differently across domains, making it hard for models to generalize when training data is scarce or unavailable. Recently, vision-language models (VLMs), such as CLIP, have shown great potential in ZSAD, but they often struggle to adapt to unseen domains due to the lack of domain-aware knowledge. To address these challenges, we propose the Domain Adaptation CLIP (DACLIP), a novel approach that adapts domain-aware knowledge to the VLM. Specifically, DACLIP leverages a Domain-Aware Knowledge Adaptation (DAKA) strategy to enhance CLIP for ZSAD across different domains. The DAKA strategy comprises multiple experts that specialize in target domains, enabling the model to dynamically select and combine specialized experts tailored to anomaly characteristics, thus improving its ability to generalize and detect a wide range of anomalies. Furthermore, we introduce learnable domain-aware prompts that are jointly learned by and injected into both the CLIP encoders (visual and text) and the DAKA modules. This dual-pathway learning enables the model to capture domain-specific features at multiple levels of the architecture, allowing for more effective adaptation to new domains and anomaly types. We evaluate our approach on several benchmark datasets spanning industrial and medical domains. Extensive experiments demonstrate that DACLIP consistently outperforms state-of-the-art methods in ZSAD, achieving significant improvements in both image-level and pixel-level anomaly detection tasks.

Reconstruction-based methods have achieved excellent performance in anomaly detection. Diffusion models are considered highly suitable for anomaly detection tasks due to their strong ability in reconstruction. Nevertheless, diffusion-based models require the reconstruction of noise features, which may lack the capacity for fine-grained feature reconstruction and fail to provide adequate semantic information for reconstruction guidance. To solve the aforementioned problems, this paper proposes a Fine-Grained Enhancement Convolutional Diffusion Transformer Anomaly Detection (FECDTAD) framework for multi-class anomaly detection. The core model of the proposed framework is the Fine-Grained Enhancement Convolutional Denoising Transformer (FECDT), which employs the diffusion transformer paradigm. To enhance fine-grained reconstruction in the diffusion process, the FECDTAD adopts a series of feature information fusion strategies. Specifically, to enhance both fine-grained perception and global understanding, the FECDT model employs a simple feature fusion module to integrate shallow-level and deep-level features extracted from a pre-trained vision transformer. To enhance the capacity for fine-grained feature reconstruction, the FECDT integrates local and global information via a CNN-Transformer architecture. Moreover, to provide guidance for the reconstruction of anomalous areas, semantic information is propagated into the FECDT through a Cross-Attention module. Experimental results demonstrate that the proposed method is effective and can surpass the state-of-the-art methods.

Deep online cross-modal hashing has gained much attention from researchers recently, as its promising applications with low storage requirement, fast retrieval efficiency and cross modality adaptive, etc. However, there still exists some technical hurdles that hinder its applications, e.g., 1) how to extract the coexistent semantic relevance of cross-modal data, 2) how to achieve competitive performance when handling the real time data streams, 3) how to transfer the knowledge learned from offline to online training in a lightweight manner. To address these problems, this paper proposes a lightweight contrastive distilled hashing (LCDH) for cross-modal retrieval, by innovatively bridging the offline and online cross-modal hashing by similarity matrix approximation in a knowledge distillation framework. Specifically, in the teacher network, LCDH first extracts the cross-modal features by CLIP, which are further fed into an attention module for representation enhancement after feature fusion. Then, the output of the attention module is fed into a FC layer to obtain hash codes for aligning the sizes of similarity matrices for online and offline training. In the student network, LCDH extracts the visual and textual features by lightweight models, and then the features are fed into a FC layer to generate binary codes. Finally, by approximating the similarity matrices, the performance of online hashing in the lightweight student network can be enhanced by the supervision of coexistent semantic relevance that is distilled from the teacher network. Experimental results on three widely used datasets demonstrate that LCDH outperforms some state-of-the-art methods.

Few-shot defect multi-classification (FSDMC) is an emerging trend in quality control within industrial manufacturing. However, current FSDMC research often lacks generalizability due to its focus on specific datasets. Additionally, defect classification heavily relies on contextual information within images, and existing methods fall short of effectively extracting this information. To address these challenges, we propose a general FSDMC framework called MVREC, which offers two primary advantages: (1) MVREC extracts general features for defect instances by incorporating the pre-trained AlphaCLIP model. (2) It utilizes a region-context framework to enhance defect features by leveraging mask region input and multi-view context augmentation. Furthermore, Few-shot Zip-Adapter(-F) classifiers within the model are introduced to cache the visual features of the support set and perform few-shot classification. We also introduce MVTec-FS, a new FSDMC benchmark based on MVTec AD, which includes 1228 defect images with instance-level mask annotations and 46 defect types. Extensive experiments conducted on MVTec-FS and four additional datasets demonstrate its effectiveness in general defect classification and its ability to incorporate contextual information to improve classification performance.

Abstract With the advantage of exploiting complementary and consensus information across multiple views, techniques for Multi‐view Clustering have attracted increasing attention in recent years. However, it is common that data on some views is not completed in real‐world applications, which brings the challenge of partial mapping between the views. To explore the information hidden in the local geometric structure and recover missing instances through mining the information hidden in existing instances, a self‐inferring incomplete multi‐view clustering algorithm is proposed. Firstly, the incomplete multi‐view data is replenished directly and exploited as variables for inferring the missing instances. And then, a feature graph constraint is united in consensus learning. Besides, a similarity graph learning method is imposed to preserve the local manifold structure. At last, the inferred instances are filled in the missing instances for learning better consensus representation in the iterative process. Extensive experiment results show that this method can improve the clustering performance compared with the state‐of‐the‐art methods.

BACKGROUND: Glaucoma can cause irreversible blindness to people's eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough. METHODS: This paper proposes a new encoder-decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder-decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup. RESULTS: The experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively. CONCLUSIONS: This paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective.