PWLR: Pairwise Witness Local Rejection for Boundary-Aware Out-of-Distribution Detection

Recent studies show that using potential out-of-distribution (OOD) labels from large corpora as auxiliary information can improve OOD detection in vision-language models (VLMs). However, these methods often fail when real-world OOD samples fall outside the predefined OOD label set. To address this limitation, we propose DynProto, a novel approach that learns OOD prototypes dynamically during testing using only in-distribution (ID) information. DynProto is inspired by a key observation: OOD samples predicted as the same ID class tend to cluster in the feature space. With this insight, we leverage easy-to-detect OOD samples as ``anchors'' to find their harder-to-detect, similar counterparts. To this end, DynProto introduces two modules: Coarse OOD Pattern Capturing Module caches OOD patterns that are easily confused with each ID class during testing, and Fine-grained OOD Pattern Refinement Module subsequently clusters these patterns within each cache and aggregates them into representative OOD prototypes. By measuring similarity to ID and dynamic OOD prototypes, DynProto enables accurate OOD detection. DynProto significantly outperforms prior methods across multiple benchmarks. On ImageNet OOD benchmark, DynProto reduces FPR95 by 11.60% and improves AUROC by 4.70%. Moreover, the framework is architecture-agnostic and can be integrated into various backbones.

Out-of-distribution (OOD) detection has emerged as a popular technique to enhance the reliability of machine learning models by identifying unexpected inputs from unknown classes. Recent progress in pre-trained vision-language models (VLMs) has enabled zero-shot OOD detection without access to in-distribution (ID) training data; in this setting, existing methods commonly treat text embeddings of class names as class prototypes. In this paper, we challenge the widely adopted text-as-prototype paradigm by theoretically showing that off-the-shelf textual prototypes are generally misaligned with the optimal visual prototypes, yielding an intrinsic modality gap that cannot be eliminated by prompt engineering alone. To mitigate this gap under the post-hoc constraint, this paper presents an online pseudo-supervised framework that directly learns class prototypes in the visual feature space using unlabeled test-time data streams and soft predictions from the pre-trained VLMs. We provide theoretical guarantees for the convergence of the online optimization procedure. Extensive experiments empirically demonstrate that our method achieves a new state of the art across a variety of OOD detection setups.

Continual vision-language models (VLMs) encounter a stream of environments and must also reject inputs that do not belong to the environment at hand. Class information from earlier environments can aid out-of-distribution (OOD) detection, yet existing detectors score each environment in isolation, and when previous environments help or hurt is theoretically unclear. We develop a theory of environment-aware OOD detection built on contribution scores, pairwise cosine expectations measuring how the image and text features of one environment align with those of another. We propose a cross-environment maximum-concept-matching (MCM) score with two terms: a primary term matching the test image against the current class texts, and a secondary term matching it against the class texts of a previous environment, weighted by the intra-modal alignment between the two environments. We prove that, under a positive environment transition and a bounded class-matching margin for OOD inputs, this score attains a false-positive rate no larger than that of the standard MCM detector at an adjusted threshold. We then show that the score gap of an MCM detector between two environments equals the self-contribution of the in-distribution environment minus its cross-modal contribution into the OOD environment, which separates soft OOD environments, with explicit AUROC, true-positive, and false-positive guarantees, from hard ones, which no threshold that retains the in-distribution data can reliably reject. For cascaded detection, in which the text prototypes of all seen environments are pooled, the false-positive rate on every past environment is non-decreasing in time, and we characterize when detection provably fails. A contribution balance factor applied to the gradients of the classification loss and of a smoothed false-positive rate completes the framework.