Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to Tail

TL;DR

This paper introduces PATT, a novel method for long-tailed out-of-distribution detection that enhances tail class features using augmentation and attention mechanisms, outperforming existing methods.

Contribution

The paper proposes a new approach using vMF distributions and feature calibration to improve OOD detection in long-tailed data without harming ID classification accuracy.

Findings

Outperforms state-of-the-art on multiple benchmarks.

Effectively enhances tail class feature representation.

Improves OOD detection accuracy significantly.

Abstract

Current out-of-distribution (OOD) detection methods typically assume balanced in-distribution (ID) data, while most real-world data follow a long-tailed distribution. Previous approaches to long-tailed OOD detection often involve balancing the ID data by reducing the semantics of head classes. However, this reduction can severely affect the classification accuracy of ID data. The main challenge of this task lies in the severe lack of features for tail classes, leading to confusion with OOD data. To tackle this issue, we introduce a novel Prioritizing Attention to Tail (PATT) method using augmentation instead of reduction. Our main intuition involves using a mixture of von Mises-Fisher (vMF) distributions to model the ID data and a temperature scaling module to boost the confidence of ID data. This enables us to generate infinite contrastive pairs, implicitly enhancing the semantics of ID classes while promoting differentiation between ID and OOD data. To further strengthen the detection of OOD data without compromising the classification performance of ID data, we propose feature calibration during the inference phase. By extracting an attention weight from the training set that prioritizes the tail classes and reduces the confidence in OOD data, we improve the OOD detection capability. Extensive experiments verified that our method outperforms the current state-of-the-art methods on various benchmarks.