Enhanced Dual-Pattern Matching With Vision-Language Representation for Out-of-Distribution Detection
文献类型：期刊论文
| 作者 | Xiang, Xiang1,2; Xu, Zhuo3; Zhang, Zihan3; Zeng, Zhigang3; Chen, Xilin4 |
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2025-11-01 |
| 卷号 | 47期号:11页码:9673-9687 |
| 关键词 | Visualization Adaptation models Training Data models Computational modeling Feature extraction Pattern matching Tuning Robustness Data mining OOD detection vision-language models |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2025.3590717 |
| 英文摘要 | Out-of-distribution (OOD) detection presents a significant challenge in deploying pattern recognition and machine learning models, as they frequently fail to generalize to data from unseen distributions. Recent advancements in vision-language models (VLMs), particularly CLIP, have demonstrated promising results in OOD detection through their rich multimodal representations. However, current CLIP-based OOD detection methods predominantly rely on single-modality in-distribution (ID) data (e.g., textual cues), overlooking the valuable information contained in ID visual cues. In this work, we demonstrate that incorporating ID visual information is crucial for unlocking CLIP's full potential in OOD detection. We propose a novel approach, Dual-Pattern Matching (DPM), which effectively adapts CLIP for OOD detection by jointly exploiting both textual and visual ID patterns. Specifically, DPM refines visual and textual features through the proposed Domain-Specific Feature Aggregation (DSFA) and Prompt Enhancement (PE) modules. Subsequently, DPM stores class-wise textual features as textual patterns and aggregates ID visual features as visual patterns. During inference, DPM calculates similarity scores relative to both patterns to identify OOD data. Furthermore, we enhance DPM with lightweight adaptation mechanisms to further boost OOD detection performance. Comprehensive experiments demonstrate that DPM surpasses state-of-the-art methods on multiple benchmarks, highlighting the effectiveness of leveraging multimodal information for OOD detection. The proposed dual-pattern approach provides a simple yet robust framework for leveraging vision-language representations in OOD detection tasks. |
| 资助项目 | The 111 Project on Computational Intelligence and Intelligent Control[B18024] ; Foundation for Outstanding Research Groups of Hubei Province[2025AFA012] ; Peng Cheng Lab[PCL2023A08] ; Natural Science Fund of Hubei Province[2022CFB823] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001587283400007 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/41620]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Xiang, Xiang |
| 作者单位 | 1.Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China 2.Peng Cheng Natl Lab, Shenzhen 518000, Peoples R China 3.HUST, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiang, Xiang,Xu, Zhuo,Zhang, Zihan,et al. Enhanced Dual-Pattern Matching With Vision-Language Representation for Out-of-Distribution Detection[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2025,47(11):9673-9687. |
| APA | Xiang, Xiang,Xu, Zhuo,Zhang, Zihan,Zeng, Zhigang,&Chen, Xilin.(2025).Enhanced Dual-Pattern Matching With Vision-Language Representation for Out-of-Distribution Detection.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,47(11),9673-9687. |
| MLA | Xiang, Xiang,et al."Enhanced Dual-Pattern Matching With Vision-Language Representation for Out-of-Distribution Detection".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 47.11(2025):9673-9687. |
入库方式： OAI收割
来源：计算技术研究所
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