A Metacognitive Approach to Out-of-Distribution Detection for Segmentation

• URL: http://arxiv.org/abs/2311.07578v1
• Date: Wed, 4 Oct 2023 16:37:38 GMT
• Title: A Metacognitive Approach to Out-of-Distribution Detection for Segmentation
• Authors: Meghna Gummadi, Cassandra Kent, Karl Schmeckpeper, and Eric Eaton
• Abstract summary: We introduce a metacognitive approach to improve out-of-distribution (OOD) detection for segmentation. Our approach incorporates a novel method of generating synthetic OOD data in context with in-distribution data. Our resulting approach can reliably detect OOD instances in a scene, as shown by state-of-the-art performance on OOD detection for semantic segmentation benchmarks.
• Score: 22.500233661061912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite outstanding semantic scene segmentation in closed-worlds, deep neural networks segment novel instances poorly, which is required for autonomous agents acting in an open world. To improve out-of-distribution (OOD) detection for segmentation, we introduce a metacognitive approach in the form of a lightweight module that leverages entropy measures, segmentation predictions, and spatial context to characterize the segmentation model's uncertainty and detect pixel-wise OOD data in real-time. Additionally, our approach incorporates a novel method of generating synthetic OOD data in context with in-distribution data, which we use to fine-tune existing segmentation models with maximum entropy training. This further improves the metacognitive module's performance without requiring access to OOD data while enabling compatibility with established pre-trained models. Our resulting approach can reliably detect OOD instances in a scene, as shown by state-of-the-art performance on OOD detection for semantic segmentation benchmarks.

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