Weakly Supervised Co-training with Swapping Assignments for Semantic Segmentation

• URL: http://arxiv.org/abs/2402.17891v2
• Date: Tue, 9 Jul 2024 18:54:17 GMT
• Title: Weakly Supervised Co-training with Swapping Assignments for Semantic Segmentation
• Authors: Xinyu Yang, Hossein Rahmani, Sue Black, Bryan M. Williams,
• Abstract summary: Class activation maps (CAMs) are commonly employed in weakly supervised semantic segmentation (WSSS) to produce pseudo-labels. We propose an end-to-end WSSS model incorporating guided CAMs, wherein our segmentation model is trained while concurrently optimizing CAMs online. CoSA is the first single-stage approach to outperform all existing multi-stage methods including those with additional supervision.
• Score: 21.345548821276097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class activation maps (CAMs) are commonly employed in weakly supervised semantic segmentation (WSSS) to produce pseudo-labels. Due to incomplete or excessive class activation, existing studies often resort to offline CAM refinement, introducing additional stages or proposing offline modules. This can cause optimization difficulties for single-stage methods and limit generalizability. In this study, we aim to reduce the observed CAM inconsistency and error to mitigate reliance on refinement processes. We propose an end-to-end WSSS model incorporating guided CAMs, wherein our segmentation model is trained while concurrently optimizing CAMs online. Our method, Co-training with Swapping Assignments (CoSA), leverages a dual-stream framework, where one sub-network learns from the swapped assignments generated by the other. We introduce three techniques: i) soft perplexity-based regularization to penalize uncertain regions; ii) a threshold-searching approach to dynamically revise the confidence threshold; and iii) contrastive separation to address the coexistence problem. CoSA demonstrates exceptional performance, achieving mIoU of 76.2\% and 51.0\% on VOC and COCO validation datasets, respectively, surpassing existing baselines by a substantial margin. Notably, CoSA is the first single-stage approach to outperform all existing multi-stage methods including those with additional supervision. Code is avilable at \url{https://github.com/youshyee/CoSA}.

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