Exploring High-order-aware Prompt Learning for Zero-shot Anomaly Detection

Many methods have demonstrated promising results in zeroshot 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.