JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning

Key points

• We overcome these challenges by proposing JMT-YOLO, a novel joint multi-task learning framework for DAOD.1
• It introduces an auxiliary semantic segmentation task and a masking mechanism to enhance the modeling of foreground-background semantic boundaries in the target domain.1
• Specifically, fine-grained feature degradation is reduced by introducing a multiscale context-aware segmentation head equipped with a dense connection structure to effectively capture local and global contextual features.1
• Extensive experiments on three benchmark domain adaptation tasks (Pascal VOC to Clipart1k, Cityscapes to Foggy Cityscapes, and KITTI to Cityscapes) demonstrate that JMT-YOLO achieves detection performances of 46.9%, 57.3%, and 61.0%, respectively.1

Sources (1)

• [1]JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 02:47 AM

  Domain adaptive object detection (DAOD) aims to reduce performance degradation due to distribution shifts between training (source) and testing (target) datasets.

  We overcome these challenges by proposing JMT-YOLO, a novel joint multi-task learning framework for DAOD.

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