ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection

• URL: http://arxiv.org/abs/2508.07819v5
• Date: Fri, 10 Oct 2025 09:58:55 GMT
• Title: ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection
• Authors: Ke Ma, Jun Long, Hongxiao Fei, Liujie Hua, Zhen Dai, Yueyi Luo,
• Abstract summary: Pre-trained Vision-Language Models (VLMs) struggle with Zero-Shot Anomaly Detection (ZSAD)<n>We propose a parameter-efficient Convolutional Low-Rank Adaptation (Conv-LoRA) adapter to inject local inductive biases for fine-grained representation.<n>We also introduce a Dynamic Fusion Gateway (DFG) that leverages visual context to adaptively modulate text prompts.
• Score: 21.26826497960086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained Vision-Language Models (VLMs) struggle with Zero-Shot Anomaly Detection (ZSAD) due to a critical adaptation gap: they lack the local inductive biases required for dense prediction and employ inflexible feature fusion paradigms. We address these limitations through an Architectural Co-Design framework that jointly refines feature representation and cross-modal fusion. Our method proposes a parameter-efficient Convolutional Low-Rank Adaptation (Conv-LoRA) adapter to inject local inductive biases for fine-grained representation, and introduces a Dynamic Fusion Gateway (DFG) that leverages visual context to adaptively modulate text prompts, enabling a powerful bidirectional fusion. Extensive experiments on diverse industrial and medical benchmarks demonstrate superior accuracy and robustness, validating that this synergistic co-design is critical for robustly adapting foundation models to dense perception tasks. The source code is available at https://github.com/cockmake/ACD-CLIP.

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