Many methods have demonstrated promising results in zero-shot anomaly detection (ZSAD) by incorporating prompt learning (PL) to fine-tune Vision-Language Models. However, the prompt learners proposed in recent studies remain relatively simple, such as learnable textual and visual prompts. Relying solely on the current PL paradigm restricts the ability to generate more precise prompts, thereby hindering improved ZSAD performance. To mitigate this issue, this paper proposes a high-order-aware prompt learning framework, termed HiPL, which facilitates the detection of unseen anomalies through generating prompts fortified by hypergraphs. Specifically, HiPL models high-order correlations among patches through a dynamically constructed hypergraph structure. Then we leverage a hypergraph semantic convolution to capture potential collaborative information by propagating high-order correlations by hyperedges. Meanwhile, HiPL introduces a Mixture-of-Experts prompt learner (MoEPLer), where the experts within MoEPLer can generate multiple distinct prompts based on the modeled high-order correlations. Then, the final high-order-aware textual prompts can be formed by synthetically considering each expert's prompt by gating weights. This enables a comprehensive understanding of potential anomalous patterns, thereby facilitating ZSAD performance. Large-scale experiments conducted on 12 datasets, spanning natural, industrial, and medical domains, demonstrate the validity of proposed HiPL.

Anomaly detection (AD) is a crucial visual task aimed at recognizing abnormal pattern within samples. However, most existing AD methods suffer from limited generalizability, as they are primarily designed for domain-specific applications, such as industrial scenarios, and often perform poorly when applied to other domains. This challenge largely stems from the inherent discrepancies in features across domains. To bridge this domain gap, we introduce UniNet, a generic unified framework that incorporates effective feature selection and contrastive learning-guided anomaly discrimination. UniNet comprises student-teacher models and a bottleneck, featuring several vital innovations: First, we propose domain-related feature selection, where the student is guided to select and focus on representative features from the teacher with domain-relevant priors, while restoring them effectively. Second, a similarity contrastive loss function is developed to strengthen the correlations among homogeneous features. Meanwhile, a margin loss function is proposed to enforce the separation between the similarities of abnormality and normality, effectively improving the model’s ability to discriminate anomalies. Third, we propose a weighted decision mechanism for dynamically evaluating the anomaly score to achieve robust AD. Large-scale experiments on 12 datasets from various domains show that UniNet surpasses existing methods.