DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks.

IEEE Transactions on Pattern Analysis and Machine Intelligence(2017)

引用 160|浏览298
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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 propo...
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关键词
Object detection,Context modeling,Deformable models,Machine learning,Visualization,Training,Neural networks
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