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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 3: AAAI-22 Technical Tracks 3

Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head

February 1, 2023

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Authors

Shida Zheng

Hikvision Research Institute


Chenshu Chen

Hikvision Research Institute


Xiaowei Cai

Hikvison Research Institute


Tingqun Ye

Hikvision Research Institute


Wenming Tan

Hikvision Research Institute


DOI:

10.1609/aaai.v36i3.20264


Abstract:

Pseudo bounding boxes from the self-training paradigm are inevitably noisy for semi-supervised object detection. To cope with that, a dual decoupling training framework is proposed in the present study, i.e. clean and noisy data decoupling, and classification and localization task decoupling. In the first decoupling, two-level thresholds are used to categorize pseudo boxes into three groups, i.e. clean backgrounds, noisy foregrounds and clean foregrounds. With a specially designed noise-bypass head focusing on noisy data, backbone networks can extract coarse but diverse information; and meanwhile, an original head learns from clean samples for more precise predictions. In the second decoupling, we take advantage of the two-head structure for better evaluation of localization quality, thus the category label and location of a pseudo box can remain independent of each other during training. The approach of two-level thresholds is also applied to group pseudo boxes into three sections of different location accuracy. We outperform existing works by a large margin on VOC datasets, reaching 54.8 mAP(+1.8), and even up to 55.9 mAP(+1.5) by leveraging MS-COCO train2017 as extra unlabeled data. On MS-COCO benchmark, our method also achieves about 1.0 mAP improvements averaging across protocols compared with the prior state-of-the-art.

Topics: AAAI

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HOW TO CITE:

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head Proceedings of the AAAI Conference on Artificial Intelligence (2022) 3526-3534.

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head AAAI 2022, 3526-3534.

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan (2022). Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head. Proceedings of the AAAI Conference on Artificial Intelligence, 3526-3534.

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan. Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.3526-3534.

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan. 2022. Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head. "Proceedings of the AAAI Conference on Artificial Intelligence". 3526-3534.

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan. (2022) "Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head", Proceedings of the AAAI Conference on Artificial Intelligence, p.3526-3534

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan, "Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head", AAAI, p.3526-3534, 2022.

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan. "Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.3526-3534.

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan. "Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 3526-3534.

Shida Zheng||Chenshu Chen||Xiaowei Cai||Tingqun Ye||Wenming Tan. Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head. AAAI[Internet]. 2022[cited 2023]; 3526-3534.


ISSN: 2374-3468


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