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Surgical instrument segmentation is crucial for computer-assisted surgery. Different from common object segmentation, it is more challenging due to the large illumination variation and scale variation in the surgical scenes. In this paper, we propose a bilinear attention network with adaptive receptive fields to address these two issues. To deal with the illumination variation, the bilinear attention module models global contexts and semantic dependencies between pixels by capturingdoi:10.24963/ijcai.2020/116 dblp:conf/ijcai/NiBWZHXLW20 fatcat:xct4te4e7ngsbgu2pcazlcnx4q