Zhao, Z., Wang, Y., Yin, Y., Zhang, Y., Yang, X., Cheng, J., Zimmermann, R., Guan, C., & Zhou, S. K. (2026). BL-UDA: Towards Unsupervised Domain-Adaptive Surgical Instrument Segmentation with Source Box Labels. In (Editor), Proceedings of the 2026 International Conference on Multimedia Retrieval. https://doi.org/10.1145/3805622.3810665
Abstract:
Recent advances in unsupervised domain adaptation (UDA) by adapting the model from one domain to another unseen domain have shown considerable promise in improving surgical instrument segmentation performance across domains. However, existing UDA methods primarily rely on pixel-wise labels, which are always difficult to collect due to the labor-intensive annotation process. In this work, we aim to relax the dependence on pixel-level supervision and investigate a challenging UDA setting - source box annotations, where weak supervision and domain shifts coexist. To achieve this, we introduce a novel unsupervised domain adaptation framework, BL-UDA, which leverages bounding box annotations for surgical instrument segmentation across domains. By utilizing the Segment Anything Model (SAM) for pseudo label generation from box annotations, our method effectively bridges object-level and pixel-level domain adaptation. The proposed BL-UDA framework comprises a teacher-student network with entropy minimization for object detection and an entropy-based label selection strategy for generating box prompts to SAM, facilitating pixel-level domain adaptation. Extensive experiments on the EndoVis 2017 and 2018 datasets demonstrate the superiority of BL-UDA over existing UDA methods, significantly mitigating domain shifts and addressing weak supervision challenges with minimal annotation requirements.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the A*STAR - RIE2030 Career Development Fund
Grant Reference no. : H26-KSR0023
This research / project is supported by the National University of Singapore - NUS Artificial Intelligence Institute
Grant Reference no. : NAII-SG-2025-027