{"id":684348,"date":"2020-08-10T13:27:55","date_gmt":"2020-08-10T20:27:55","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=684348"},"modified":"2020-08-10T13:27:55","modified_gmt":"2020-08-10T20:27:55","slug":"boosting-weakly-supervised-object-detection-with-progressive-knowledge-transfer","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/boosting-weakly-supervised-object-detection-with-progressive-knowledge-transfer\/","title":{"rendered":"Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer"},"content":{"rendered":"

In this paper, we propose an effective knowledge transfer framework to boost the weakly supervised object detection accuracy with the help of an external fully-annotated source dataset, whose categories may not overlap with the target domain. This setting is of great practical value due to the existence of many off-the-shelf detection datasets. To more effectively utilize the source dataset, we propose to iteratively transfer the knowledge from the source domain by a one-class universal detector and learn the target-domain detector. The box-level pseudo ground truths mined by the target-domain detector in each iteration effectively improve the one-class universal detector. Therefore, the knowledge in the source dataset is more thoroughly exploited and leveraged. Extensive experiments are conducted with Pascal VOC 2007 as the target weakly-annotated dataset and COCO\/ImageNet as the source fully-annotated dataset. With the proposed solution, we achieved an mAP of 59.7% detection performance on the VOC test set and an mAP of 60.2% after retraining a fully supervised Faster RCNN with the mined pseudo ground truths. This is significantly better than any previously known results in related literature and sets a new state-of-the-art of weakly supervised object detection under the knowledge transfer setting. Code is available on GitHub (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this paper, we propose an effective knowledge transfer framework to boost the weakly supervised object detection accuracy with the help of an external fully-annotated source dataset, whose categories may not overlap with the target domain. This setting is of great practical value due to the existence of many off-the-shelf detection datasets. To more effectively 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Zhong","user_id":0,"rest_url":false},{"type":"text","value":"Jianfeng Wang","user_id":0,"rest_url":false},{"type":"text","value":"Jian Peng","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Lei Zhang","user_id":32641,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Lei 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