Zero-Shot Out-of-Distribution Detection with Outlier Label Exposure

TL;DR

This paper introduces Outlier Label Exposure (OLE), a novel method leveraging auxiliary outlier class labels and prototypes to significantly improve zero-shot out-of-distribution detection with vision-language models like CLIP.

Contribution

The paper proposes a new OOD detection approach using outlier label prompts and prototype learning, addressing label noise and calibration issues for enhanced zero-shot detection.

Findings

OLE achieves state-of-the-art performance on large-scale OOD benchmarks.

Outlier prototypes improve the separation between ID and OOD data.

Synthesizing outlier prototypes enhances detection calibration.

Abstract

As vision-language models like CLIP are widely applied to zero-shot tasks and gain remarkable performance on in-distribution (ID) data, detecting and rejecting out-of-distribution (OOD) inputs in the zero-shot setting have become crucial for ensuring the safety of using such models on the fly. Most existing zero-shot OOD detectors rely on ID class label-based prompts to guide CLIP in classifying ID images and rejecting OOD images. In this work we instead propose to leverage a large set of diverse auxiliary outlier class labels as pseudo OOD class text prompts to CLIP for enhancing zero-shot OOD detection, an approach we called Outlier Label Exposure (OLE). The key intuition is that ID images are expected to have lower similarity to these outlier class prompts than OOD images. One issue is that raw class labels often include noise labels, e.g., synonyms of ID labels, rendering raw OLE-based detection ineffective. To address this issue, we introduce an outlier prototype learning module that utilizes the prompt embeddings of the outlier labels to learn a small set of pivotal outlier prototypes for an embedding similarity-based OOD scoring. Additionally, the outlier classes and their prototypes can be loosely coupled with the ID classes, leading to an inseparable decision region between them. Thus, we also introduce an outlier label generation module that synthesizes our outlier prototypes and ID class embeddings to generate in-between outlier prototypes to further calibrate the detection in OLE. Despite its simplicity, extensive experiments show that OLE substantially improves detection performance and achieves new state-of-the-art performance in large-scale OOD and hard OOD detection benchmarks.