# Learning Transferable Negative Prompts for Out-of-Distribution Detection

arXiv: https://arxiv.org/abs/2404.03248 · HTML: https://mlsanity.com/paper/2404.03248/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 56 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [ViM: Out-Of-Distribution with Virtual-logit Matching](https://mlsanity.com/paper/2203.10807/) (2022)
- [On the Importance of Gradients for Detecting Distributional Shifts in the Wild](https://mlsanity.com/paper/2110.00218/) (2021)
- [Factual Probing Is [MASK]: Learning vs. Learning to Recall](https://mlsanity.com/paper/2104.05240/) (2021)
- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space](https://mlsanity.com/paper/2105.01879/) (2021)

## Built on by (most cited first, 30 of 43)

- [Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey](https://mlsanity.com/paper/2407.21794/) (2024)
- [Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances](https://mlsanity.com/paper/2409.11884/) (2024)
- [AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models](https://mlsanity.com/paper/2410.20149/) (2024)
- [Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection](https://mlsanity.com/paper/2411.03359/) (2024)
- [CountGD++: Generalized Prompting for Open-World Counting](https://mlsanity.com/paper/2512.23351/) (2025)
- [OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning](https://mlsanity.com/paper/2407.06045/) (2024)
- [Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection](https://mlsanity.com/paper/2510.05782/) (2025)
- [Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey](https://mlsanity.com/paper/2505.02448/) (2025)
- [Zero-Shot Out-of-Distribution Detection with Outlier Label Exposure](https://mlsanity.com/paper/2406.01170/) (2024)
- [From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects](https://mlsanity.com/paper/2411.18207/) (2024)
- [Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detection](https://mlsanity.com/paper/2510.10584/) (2025)
- [Revisiting Logit Distributions for Reliable Out-of-Distribution Detection](https://mlsanity.com/paper/2510.20134/) (2025)
- [Boosting Few-Shot Open-Set Object Detection via Prompt Learning and Robust Decision Boundary](https://mlsanity.com/paper/2406.18443/) (2024)
- [A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language Models](https://mlsanity.com/paper/2501.18463/) (2025)
- [Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimation](https://mlsanity.com/paper/2502.00662/) (2025)
- [FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection](https://mlsanity.com/paper/2507.04511/) (2025)
- [T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-World](https://mlsanity.com/paper/2603.18481/) (2026)
- [NegRefine: Refining Negative Label-Based Zero-Shot OOD Detection](https://mlsanity.com/paper/2507.09795/) (2025)
- [Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detection](https://mlsanity.com/paper/2508.17667/) (2025)
- [Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems](https://mlsanity.com/paper/2510.21254/) (2025)
- [TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models](https://mlsanity.com/paper/2604.15756/) (2026)
- [LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data](https://mlsanity.com/paper/2505.12952/) (2025)
- [Unlearning the Noisy Correspondence Makes CLIP More Robust](https://mlsanity.com/paper/2507.03434/) (2025)
- [Generalized Few-Shot Out-of-Distribution Detection](https://mlsanity.com/paper/2508.05732/) (2025)
- [An Empirical Analysis of VLM-based OOD Detection: Mechanisms, Advantages, and Sensitivity](https://mlsanity.com/paper/2509.13375/) (2025)
- [Delta Energy: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization](https://mlsanity.com/paper/2510.11296/) (2025)
- [EraseAnything++: Enabling Concept Erasure in Rectified Flow Transformers Leveraging Multi-Object Optimization](https://mlsanity.com/paper/2603.00978/) (2026)
- [Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs](https://mlsanity.com/paper/2603.02618/) (2026)
- [PROBE: Diagnosing Residual Concept Capacity in Erased Text-to-Video Diffusion Models](https://mlsanity.com/paper/2603.21547/) (2026)
- [Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Models](https://mlsanity.com/paper/2605.23797/) (2026)

## Abstract (arXiv)

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named 'NegPrompt', to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external outlier data. Further, current methods assume the availability of samples of all ID classes, rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast, our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://github.com/mala-lab/negprompt.