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Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance of detecting out-of-distribution (OOD) samples in the open world. In this work, we propose a novel feature-space OOD detection score based on class-specific and class-agnostic information. Specifically, the approach utilizes Whitened Linear Discriminant Analysis to project features into two subspaces the discriminative and residual subspaces - for which the in-distribution (ID) classes are maximally separated and closely clustered, respectively. The OOD score is then determined by combining the deviation from the input data to the ID pattern in both subspaces. The efficacy of our method, named WDiscOOD, is verified on the large-scale ImageNet-1k benchmark, with six OOD datasets that cover a variety of distribution shifts. WDiscOOD demonstrates superior performance on deep classifiers with diverse backbone architectures, including CNN and vision transformer. Furthermore, we also show that WDiscOOD more effectively detects novel concepts in representation spaces trained with contrastive objectives, including supervised contrastive loss and multi-modality contrastive loss.
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This content will become publicly available on March 6, 2027
Improving Out-of-Distribution Detection using Segmented Images and Cross-View Attention Fusion
Although out-of-distribution (OOD) detection has been extensively studied, it continues to face challenges in handling OOD data semantically similar to In-Distribution (ID) data. Part of the difficulty arises from the model's inability to learn superior ID class discriminative features. We propose to improve this by segmenting input images into foreground and background views and combining them with the original input image (original view) in a multi-view learning approach. We present a novel method, called CASOD (Cross-view Attention of Segmented views for OOD Detection), that learns better discriminative information from all three views and subsequently, fuses them through a novel stacked cross-view attention mechanism to produce the final predictive feature representation. A feature-based method is then applied to the final fused feature for OOD detection, giving major improvements over a range of strong baselines on various near-and far-OOD datasets. CASOD achieves state-of-the-art performance in various experimental settings with challenging ID and OOD datasets. Code for CASOD can be found at https://github. com/alexp205/CASOD.
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- Award ID(s):
- 2229876
- PAR ID:
- 10707506
- Publisher / Repository:
- Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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