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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 2: AAAI-21 Technical Tracks 2

GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates

February 1, 2023

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Authors

Qifei Jia

Institute of Information Science, Beijing Jiaotong University, Beijing, China Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China


Shikui Wei

Institute of Information Science, Beijing Jiaotong University, Beijing, China Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China


Tao Ruan

Institute of Information Science, Beijing Jiaotong University, Beijing, China Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China


Yufeng Zhao

China Academy of Chinese Medical Sciences, Beijing, China


Yao Zhao

Institute of Information Science, Beijing Jiaotong University, Beijing, China Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China


DOI:

10.1609/aaai.v35i2.16261


Abstract:

Weakly-Supervised Object Detection (WSOD) aims at training a model with limited and coarse annotations for precisely locating the regions of objects. Existing works solve the WSOD problem by using a two-stage framework, i.e., generating candidate bounding boxes with weak supervision information and then refining them by directly employing supervised object detection models. However, most of such works mainly focus on the performance boosting of the first stage, while ignoring the better usage of generated candidate bounding boxes. To address this issue, we propose a new two-stage framework for WSOD, named GradingNet, which can make good use of the generated candidate bounding boxes. Specifically, the proposed GradingNet consists of two modules: Boxes Grading Module (BGM) and Informative Boosting Module (IBM). BGM generates proposals of the bounding boxes by using standard one-stage weakly-supervised methods, then utilizes Inclusion Principle to pick out highly-reliable boxes and evaluate the grade of each box. With the above boxes and their grade information, an effective anchor generator and a grade-aware loss are carefully designed to train the IBM. Taking the advantages of the grade information, our GradingNet achieves state-of-the-art performance on COCO, VOC 2007 and VOC 2012 benchmarks.

Topics: AAAI

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HOW TO CITE:

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates Proceedings of the AAAI Conference on Artificial Intelligence (2021) 1682-1690.

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates AAAI 2021, 1682-1690.

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao (2021). GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates. Proceedings of the AAAI Conference on Artificial Intelligence, 1682-1690.

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao. GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.1682-1690.

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao. 2021. GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates. "Proceedings of the AAAI Conference on Artificial Intelligence". 1682-1690.

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao. (2021) "GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates", Proceedings of the AAAI Conference on Artificial Intelligence, p.1682-1690

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao, "GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates", AAAI, p.1682-1690, 2021.

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao. "GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.1682-1690.

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao. "GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 1682-1690.

Qifei Jia||Shikui Wei||Tao Ruan||Yufeng Zhao||Yao Zhao. GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates. AAAI[Internet]. 2021[cited 2023]; 1682-1690.


ISSN: 2374-3468


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