Frequency-based Matcher for Long-tailed Semantic Segmentation

• URL: http://arxiv.org/abs/2406.03917v1
• Date: Thu, 6 Jun 2024 09:57:56 GMT
• Title: Frequency-based Matcher for Long-tailed Semantic Segmentation
• Authors: Shan Li, Lu Yang, Pu Cao, Liulei Li, Huadong Ma,
• Abstract summary: We focus on a relatively under-explored task setting, long-tailed semantic segmentation (LTSS) We propose a dual-metric evaluation system and construct the LTSS benchmark to demonstrate the performance of semantic segmentation methods and long-tailed solutions. We also propose a transformer-based algorithm to improve LTSS, frequency-based matcher, which solves the oversuppression problem by one-to-many matching.
• Score: 22.199174076366003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The successful application of semantic segmentation technology in the real world has been among the most exciting achievements in the computer vision community over the past decade. Although the long-tailed phenomenon has been investigated in many fields, e.g., classification and object detection, it has not received enough attention in semantic segmentation and has become a non-negligible obstacle to applying semantic segmentation technology in autonomous driving and virtual reality. Therefore, in this work, we focus on a relatively under-explored task setting, long-tailed semantic segmentation (LTSS). We first establish three representative datasets from different aspects, i.e., scene, object, and human. We further propose a dual-metric evaluation system and construct the LTSS benchmark to demonstrate the performance of semantic segmentation methods and long-tailed solutions. We also propose a transformer-based algorithm to improve LTSS, frequency-based matcher, which solves the oversuppression problem by one-to-many matching and automatically determines the number of matching queries for each class. Given the comprehensiveness of this work and the importance of the issues revealed, this work aims to promote the empirical study of semantic segmentation tasks. Our datasets, codes, and models will be publicly available.

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