Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2023

Abstract

Semantic segmentation models classify pixels into a set of known ("in-distribution") visual classes. When deployed in an open world, the reliability of these models depends on their ability to not only classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD detection performance of these models has motivated the design of methods based on model re-training using synthetic training images that include OoD visual objects. Although successful, these re-trained methods have two issues: 1) their in-distribution segmentation accuracy may drop during re-training, and 2) their OoD detection accuracy does not generalise well to new contexts (e.g., country surroundings) outside the training set (e.g., city surroundings). In this paper, we mitigate these issues with: (i) a new residual pattern learning (RPL) module that assists the segmentation model to detect OoD pixels with minimal deterioration to the inlier segmentation performance; and (ii) a novel context-robust contrastive learning (CoroCL) that enforces RPL to robustly detect OoD pixels in various contexts. Our approach improves by around 10% FPR and 7% AuPRC the previous state-of-the-art in Fishyscapes, Segment-Me-If-You-Can, and RoadAnomaly datasets. Code will be available.

Keywords

Semantic segmentation model, Out-of-distribution detection

Discipline

Artificial Intelligence and Robotics

Research Areas

Cybersecurity; Intelligent Systems and Optimization

Publication

Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, October 2-6

First Page

1151

Last Page

1161

Publisher

IEEE

City or Country

New York, NY, USA

Copyright Owner and License

Authors

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