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SC-RPN: A Strong Correlation Learning Framework for Region Proposal

Abstract : Current state-of-the-art two-stage detectors heavily rely on region proposals to guide the accurate detection for objects. In previous region proposal approaches, the interaction between different functional modules is correlated weakly, which limits or decreases the performance of region proposal approaches. In this paper, we propose a novel two-stage strong correlation learning framework, abbreviated as SC-RPN, which aims to set up stronger relationship among different modules in the region proposal task. Firstly, we propose a Light-weight IoU-Mask branch to predict intersection-over-union (IoU) mask and refine region classification scores as well, it is used to prevent high-quality region proposals from being filtered. Furthermore, a sampling strategy named Size-Aware Dynamic Sampling (SADS) is proposed to ensure sampling consistency between different stages. In addition, point-based representation is exploited to generate region proposals with stronger fitting ability. Without bells and whistles, SC-RPN achieves AR(1000) 14.5% higher than that of Region Proposal Network (RPN), surpassing all the existing region proposal approaches. We also integrate SC-RPN into Fast R-CNN and Faster R-CNN to test its effectiveness on object detection task, the experimental results achieve a gain of 3.2% and 3.8% in terms of mAP compared to the original ones.
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Contributor : Laurent Jonchère Connect in order to contact the contributor
Submitted on : Friday, June 11, 2021 - 2:34:58 PM
Last modification on : Friday, June 11, 2021 - 2:35:06 PM
Long-term archiving on: : Sunday, September 12, 2021 - 7:48:55 PM


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Wenbin Zou, Zhengyu Zhang, Yingqing Peng, Canqun Xiang, Shishun Tian, et al.. SC-RPN: A Strong Correlation Learning Framework for Region Proposal. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2021, 30, pp.4084-4098. ⟨10.1109/TIP.2021.3069547⟩. ⟨hal-03229116⟩



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