Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models

• URL: http://arxiv.org/abs/2511.10923v1
• Date: Fri, 14 Nov 2025 03:24:09 GMT
• Title: Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models
• Authors: Zhixia He, Chen Zhao, Minglai Shao, Xintao Wu, Xujiang Zhao, Dong Li, Qin Tian, Linlin Yu,
• Abstract summary: Out-of-distribution (OOD) detection is committed to delineating the classification boundaries between in-distribution (ID) and OOD images.<n>Negative prompts are introduced to emphasize the dissimilarity between image features and prompt content.<n>We propose Positive and Negative Prompt Supervision, which encourages negative prompts to capture inter-class features.
• Score: 33.39682202143465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-distribution (OOD) detection is committed to delineating the classification boundaries between in-distribution (ID) and OOD images. Recent advances in vision-language models (VLMs) have demonstrated remarkable OOD detection performance by integrating both visual and textual modalities. In this context, negative prompts are introduced to emphasize the dissimilarity between image features and prompt content. However, these prompts often include a broad range of non-ID features, which may result in suboptimal outcomes due to the capture of overlapping or misleading information. To address this issue, we propose Positive and Negative Prompt Supervision, which encourages negative prompts to capture inter-class features and transfers this semantic knowledge to the visual modality to enhance OOD detection performance. Our method begins with class-specific positive and negative prompts initialized by large language models (LLMs). These prompts are subsequently optimized, with positive prompts focusing on features within each class, while negative prompts highlight features around category boundaries. Additionally, a graph-based architecture is employed to aggregate semantic-aware supervision from the optimized prompt representations and propagate it to the visual branch, thereby enhancing the performance of the energy-based OOD detector. Extensive experiments on two benchmarks, CIFAR-100 and ImageNet-1K, across eight OOD datasets and five different LLMs, demonstrate that our method outperforms state-of-the-art baselines.

Related papers

• Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs [80.03370593724422]
  Out-of-distribution (OOD) detection seeks to identify samples from unknown classes.<n>Current methods often incorporate intra-modal distance during OOD detection, such as comparing negative texts with ID labels.<n>We propose InterNeg, a framework that systematically utilizes consistent inter-modal distance enhancement from textual and visual perspectives.
  arXiv  Detail & Related papers  (2026-03-03T05:44:47Z)
• PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding [85.22047087898311]
  We introduce Polarity-Prompt Contrastive Decoding (PromptCD), a test-time behavior control method that generalizes contrastive decoding to broader enhancement settings.<n>PromptCD constructs paired positive and negative guiding prompts for a target behavior and contrasts model responses to reinforce desirable outcomes.<n>Experiments on the "3H" alignment objectives demonstrate consistent and substantial improvements, indicating that post-trained models can achieve meaningful self-enhancement purely at test time.
  arXiv  Detail & Related papers  (2026-02-24T08:56:52Z)
• GOOD: Training-Free Guided Diffusion Sampling for Out-of-Distribution Detection [61.96025941146103]
  GOOD is a novel framework that guides sampling trajectories towards OOD regions using off-the-shelf in-distribution (ID) classifiers.<n> GOOD incorporates dual-level guidance: Image-level guidance based on the gradient of log partition to reduce input likelihood, drives samples toward low-density regions in pixel space.<n>We introduce a unified OOD score that adaptively combines image and feature discrepancies, enhancing detection robustness.
  arXiv  Detail & Related papers  (2025-10-20T03:58:46Z)
• Knowledge Regularized Negative Feature Tuning of Vision-Language Models for Out-of-Distribution Detection [54.433899174017185]
  Out-of-distribution (OOD) detection is crucial for building reliable machine learning models.<n>We propose a novel method called Knowledge Regularized Negative Feature Tuning (KR-NFT)<n>NFT applies distribution-aware transformations to pre-trained text features, effectively separating positive and negative features into distinct spaces.<n>When trained with few-shot samples from ImageNet dataset, KR-NFT not only improves ID classification accuracy and OOD detection but also significantly reduces the FPR95 by 5.44%.
  arXiv  Detail & Related papers  (2025-07-26T07:44:04Z)
• Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection [11.277049921075026]
  Out-of-distribution (OOD) detection has seen significant advancements with zero-shot approaches.<n>We propose a novel strategy to enhance zero-shot OOD detection performances for both Far-OOD and Near-OOD scenarios.<n>We introduce novel few-shot prompt tuning and visual prompt tuning to adapt the proposed framework to better align with the target distribution.
  arXiv  Detail & Related papers  (2025-01-09T13:36:37Z)
• AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models [15.754054667010468]
  Pre-trained vision-language models are effective at identifying out-of-distribution (OOD) samples by using negative labels as guidance. We introduce textitadaptive negative proxies, which are dynamically generated during testing by exploring actual OOD images. Our approach significantly outperforms existing methods, with a 2.45% increase in AUROC and a 6.48% reduction in FPR95.
  arXiv  Detail & Related papers  (2024-10-26T11:20:02Z)
• TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning [26.446233594630087]
  We propose textbfTagOOD, a novel approach for OOD detection using vision-language representations. TagOOD trains a lightweight network on the extracted object features to learn representative class centers. These centers capture the central tendencies of IND object classes, minimizing the influence of irrelevant image features during OOD detection.
  arXiv  Detail & Related papers  (2024-08-28T06:37:59Z)
• Negative Label Guided OOD Detection with Pretrained Vision-Language Models [96.67087734472912]
  Out-of-distribution (OOD) detection aims at identifying samples from unknown classes. We propose a novel post hoc OOD detection method, called NegLabel, which takes a vast number of negative labels from extensive corpus databases.
  arXiv  Detail & Related papers  (2024-03-29T09:19:52Z)
• Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection [67.68030805755679]
  Large language models (LLMs) encode a wealth of world knowledge and can be prompted to generate descriptive features for each class. In this paper, we propose to apply world knowledge to enhance OOD detection performance through selective generation from LLMs.
  arXiv  Detail & Related papers  (2023-10-12T04:14:28Z)
• How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models? [29.75562085178755]
  We study how fine-tuning impact OOD detection for few-shot downstream tasks. Our results suggest that a proper choice of OOD scores is essential for CLIP-based fine-tuning. We also show that prompt learning demonstrates the state-of-the-art OOD detection performance over the zero-shot counterpart.
  arXiv  Detail & Related papers  (2023-06-09T17:16:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.