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Semantic segmentation is a hot topic in computer vision where the mostchallenging tasks of object detection and recognition have been handling by thesuccess of semantic segmentation approaches. We propose a concept ofobject-by-object learning technique to detect 11 types of facial skin lesionsusing semantic segmentation methods. Detecting individual skin lesion in adense group is a challenging task, because of ambiguities in the appearance ofthe visual data. We observe that there exist co-occurrent visual relationsbetween object classes (e.g., wrinkle and age spot, or papule and whitehead,etc.). In fact, rich contextual information significantly helps to handle theissue. Therefore, we propose REthinker blocks that are composed of the locallyconstructed convLSTM/Conv3D layers and SE module as a one-shot attentionmechanism whose responsibility is to increase network's sensitivity in thelocal and global contextual representation that supports to capture ambiguouslyappeared objects and co-occurrence interactions between object classes.Experiments show that our proposed model reached MIoU of 79.46% on the test ofa prepared dataset, representing a 15.34% improvement over Deeplab v3+ (MIoU of64.12%).

RethNet: Object-by-Object Learning for Detecting Facial Skin Problems