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We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features.
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Scalable, high-quality object detection
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Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
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Features in concert: Discriminative feature selection meets unsupervised clustering
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2014
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The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection
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J. Guo and S. Gould · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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You only look once: Unified, real-time object detection
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Faster r-cnn: Towards real-time object detection with region proposal networks
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Object detection networks on convolutional feature maps
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segdeepm: Exploiting segmentation and context in deep neural networks for object detection
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