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Negative Label Guided OOD Detection with Pretrained Vision-Language Models

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arxiv 2403.20078 v1 pith:V33FNGCG submitted 2024-03-29 cs.CV cs.LG

Xue Jiang , Feng Liu , Zhen Fang , Hong Chen , Tongliang Liu , Feng Zheng , Bo Han This is my paper · ORCID
classification cs.CVcs.LG
keywords detectionnegativeneglabelextensivelabelsmethodmodelsinformation
verification ladder T0 review T1 audit T2 compute T3 formal
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Out-of-distribution (OOD) detection aims at identifying samples from unknown classes, playing a crucial role in trustworthy models against errors on unexpected inputs. Extensive research has been dedicated to exploring OOD detection in the vision modality. Vision-language models (VLMs) can leverage both textual and visual information for various multi-modal applications, whereas few OOD detection methods take into account information from the text modality. In this paper, we propose a novel post hoc OOD detection method, called NegLabel, which takes a vast number of negative labels from extensive corpus databases. We design a novel scheme for the OOD score collaborated with negative labels. Theoretical analysis helps to understand the mechanism of negative labels. Extensive experiments demonstrate that our method NegLabel achieves state-of-the-art performance on various OOD detection benchmarks and generalizes well on multiple VLM architectures. Furthermore, our method NegLabel exhibits remarkable robustness against diverse domain shifts. The codes are available at https://github.com/tmlr-group/NegLabel.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Representation Trajectories Matters: Complementary Evidence for OOD Detection and Image Classification

    cs.CV 2026-07 accept novelty 6.0 of 10

    Recording how an image's representation evolves block-by-block, relative to learned class routes, improves OOD detection in 131/152 comparisons and clean classification in 71/72 model–dataset cases.

  2. Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SynOOD generates synthetic near-boundary OOD images with MLLM-guided inpainting and energy-score gradients, then fine-tunes CLIP image and text features, reporting state-of-the-art OOD detection on ImageNet benchmarks.

  3. The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model

    cs.LG 2026-07 conditional novelty 5.0 of 10

    CLIP embeddings are modeled as a mixture of von Mises-Fisher distributions on the unit sphere, improving out-of-distribution detection and semantic decomposition over single-Gaussian baselines.

  4. $\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization

    cs.CV 2025-10 reject novelty 5.0 of 10

    ΔEnergy, an energy-change OOD score for CLIP, and its EBM fine-tuning loss simultaneously improve OOD detection and covariate-shift generalization.

  5. Knowledge Regularized Negative Feature Tuning of Vision-Language Models for Out-of-Distribution Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    KR-NFT tunes CLIP text features with image-conditioned scaling and shifting plus a knowledge regularization loss, improving OOD detection on base and unseen classes without forgetting pre-trained knowledge.

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