ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution
Detection in Segmentation
- URL: http://arxiv.org/abs/2309.05994v2
- Date: Sat, 28 Oct 2023 19:37:02 GMT
- Title: ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution
Detection in Segmentation
- Authors: Zhitong Gao, Shipeng Yan, Xuming He
- Abstract summary: We propose a dual-level OOD detection framework to handle domain shift and semantic shift jointly.
The first level distinguishes whether domain shift exists in the image by leveraging global low-level features.
The second level identifies pixels with semantic shift by utilizing dense high-level feature maps.
- Score: 22.084967085509387
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in dense out-of-distribution (OOD) detection have
primarily focused on scenarios where the training and testing datasets share a
similar domain, with the assumption that no domain shift exists between them.
However, in real-world situations, domain shift often exits and significantly
affects the accuracy of existing out-of-distribution (OOD) detection models. In
this work, we propose a dual-level OOD detection framework to handle domain
shift and semantic shift jointly. The first level distinguishes whether domain
shift exists in the image by leveraging global low-level features, while the
second level identifies pixels with semantic shift by utilizing dense
high-level feature maps. In this way, we can selectively adapt the model to
unseen domains as well as enhance model's capacity in detecting novel classes.
We validate the efficacy of our proposed method on several OOD segmentation
benchmarks, including those with significant domain shifts and those without,
observing consistent performance improvements across various baseline models.
Code is available at
${\href{https://github.com/gaozhitong/ATTA}{https://github.com/gaozhitong/ATTA}}$.
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