ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918411994398720 |
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| author | Ma, Ke Long, Jun Fei, Hongxiao Hua, Liujie Dai, Zhen Luo, Yueyi |
| author_facet | Ma, Ke Long, Jun Fei, Hongxiao Hua, Liujie Dai, Zhen Luo, Yueyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07819 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Ma, Ke Long, Jun Fei, Hongxiao Hua, Liujie Dai, Zhen Luo, Yueyi Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.07819 |