Enhanced Dual-Pattern Matching with Vision-Language Representation for out-of-Distribution Detection.

Abstract

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.