Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation

• URL: http://arxiv.org/abs/2412.06470v1
• Date: Mon, 09 Dec 2024 13:15:52 GMT
• Title: Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation
• Authors: Fei Wu, Pablo Marquez-Neila, Hedyeh Rafi-Tarii, Raphael Sznitman,
• Abstract summary: Multi-class semantic segmentation remains a cornerstone challenge in computer vision. Active Learning (AL) mitigates this challenge by selecting data points for annotation strategically. We present OREAL, a novel patch-based AL method designed for multi-class semantic segmentation.
• Score: 11.077512630548153
• License:
• Abstract: Multi-class semantic segmentation remains a cornerstone challenge in computer vision. Yet, dataset creation remains excessively demanding in time and effort, especially for specialized domains. Active Learning (AL) mitigates this challenge by selecting data points for annotation strategically. However, existing patch-based AL methods often overlook boundary pixels critical information, essential for accurate segmentation. We present OREAL, a novel patch-based AL method designed for multi-class semantic segmentation. OREAL enhances boundary detection by employing maximum aggregation of pixel-wise uncertainty scores. Additionally, we introduce one-vs-rest entropy, a novel uncertainty score function that computes class-wise uncertainties while achieving implicit class balancing during dataset creation. Comprehensive experiments across diverse datasets and model architectures validate our hypothesis.

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