DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks
Publication in refereed journal

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摘要In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [1] , which was the state-of-the-art, from 31 to 50.3 percent on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1 percent. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provides a global view for people to understand the deep learning object detection pipeline.
著者W. Ouyang, X. Zeng, X. Wang, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, H. Li, K. Wang, J. Yan, C.-C. Loy, X. Tang
期刊名稱IEEE Transactions on Pattern Analysis and Machine Intelligence
出版年份2017
月份7
卷號39
期次7
頁次1320 - 1334
國際標準期刊號0162-8828
電子國際標準期刊號1939-3539
語言美式英語

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