CaT: Weakly Supervised Object Detection with Category Transfer

• URL: http://arxiv.org/abs/2108.07487v1
• Date: Tue, 17 Aug 2021 07:59:34 GMT
• Title: CaT: Weakly Supervised Object Detection with Category Transfer
• Authors: Tianyue Cao, Lianyu Du, Xiaoyun Zhang, Siheng Chen, Ya Zhang, Yan-Feng Wang
• Abstract summary: A large gap exists between fully-supervised object detection and weakly-supervised object detection. We propose a novel category transfer framework for weakly supervised object detection. Our framework achieves 63.5% mAP and 80.3% CorLoc with 5 categories overlapping between two datasets.
• Score: 41.34509685442456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 visually-discriminative and semantically-correlated category information in the fully-supervised dataset to enhance the object-classification ability of a weakly-supervised detector. To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets. To handle non-overlapping category transfer, we propose a semantic graph convolutional network to promote the aggregation of semantic features between correlated categories. Experiments are conducted with Pascal VOC 2007 as the target weakly-supervised dataset and COCO as the source fully-supervised dataset. Our category transfer framework achieves 63.5% mAP and 80.3% CorLoc with 5 overlapping categories between two datasets, which outperforms the state-of-the-art methods. Codes are avaliable at https://github.com/MediaBrain-SJTU/CaT.

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