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arXiv Computer Vision By Zhenhua Yu, Jiahao Zhang, Wenchao Zhang, Ou Ye, Hengxiang Zhao

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

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JMT-YOLO is a new joint multi-task learning framework for domain adaptive object detection that builds on the Mean Teacher architecture. It adds an auxiliary semantic segmentation task and a masking mechanism to better model foreground–background boundaries and improve instance-level alignment. Experiments on Pascal VOC→Clipart1k, Cityscapes→Foggy Cityscapes, and KITTI→Cityscapes show that JMT‑YOLO outperforms existing state‑of‑the‑art methods, achieving 46.9%, 57.3%, and 61.0% detection performance respectively.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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