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MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
[article]
2022
arXiv
pre-print
Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student network by the generated supervisory signal from the teacher. Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information. Especially when extending SSL to semi-supervised object detection (SSOD), many strong augmentation methodologies related to image geometry and
arXiv:2111.10958v2
fatcat:bidangp525aedaxh3sprx4cpgy