Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection
Fanhu Zeng, Zhen Cheng, Fei Zhu, Hongxin Wei, Xu-Yao Zhang

TL;DR
This paper introduces Local-Prompt, a novel approach for out-of-distribution detection that emphasizes local information through a coarse-to-fine tuning paradigm, significantly improving detection performance in few-shot scenarios.
Contribution
The paper proposes a new Local-Prompt method that utilizes local prompts and regional regularization, enhancing OOD detection by leveraging local outlier knowledge while maintaining compatibility with global prompts.
Findings
Reduces average FPR95 by 5.17% in 4-shot setting on ImageNet-1k.
Outperforms state-of-the-art methods even with fewer shots.
Effectively integrates local prompts with global prompts during inference.
Abstract
Out-of-Distribution (OOD) detection, aiming to distinguish outliers from known categories, has gained prominence in practical scenarios. Recently, the advent of vision-language models (VLM) has heightened interest in enhancing OOD detection for VLM through few-shot tuning. However, existing methods mainly focus on optimizing global prompts, ignoring refined utilization of local information with regard to outliers. Motivated by this, we freeze global prompts and introduce Local-Prompt, a novel coarse-to-fine tuning paradigm to emphasize regional enhancement with local prompts. Our method comprises two integral components: global prompt guided negative augmentation and local prompt enhanced regional regularization. The former utilizes frozen, coarse global prompts as guiding cues to incorporate negative augmentation, thereby leveraging local outlier knowledge. The latter employs trainable…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Fault Detection and Control Systems · Distributed Sensor Networks and Detection Algorithms
MethodsFocus
