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CaT: Weakly Supervised Object Detection with Category Transfer
[article]
2021
arXiv
pre-print
A large gap exists between fully-supervised object detection and weakly-supervised object detection. To narrow this gap, some methods consider knowledge transfer from additional fully-supervised dataset. But these methods do not fully exploit discriminative category information in the fully-supervised dataset, thus causing low mAP. To solve this issue, we propose a novel category transfer framework for weakly supervised object detection. The intuition is to fully leverage both
arXiv:2108.07487v1
fatcat:fmjdq3lto5dynd2xaja2fnyocq