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Deep region-based object detector consists of a region proposal step and a deep object recognition step.
K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, TPAMI 37 (9) (2015) 1904–1916
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R. Girshick, Fast r-cnn, in: ICCV, 2015
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S. Ren, K. He, R. B. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks, in: NIPS, 2015
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A. Ghodrati, A. Diba, M. Pedersoli, T. Tuytelaars, L. Van Gool, Deepproposal: Hunting objects by cascading deep convolutional layers, in: ICCV, 2015
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S. Gidaris, N. Komodakis, Object detection via a multi-region and semantic segmentation-aware cnn model, in: ICCV, 2015
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X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, R. Urtasun, 3d object proposals for accurate object class detection, in: NIPS, 2015
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W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, C.-C. Loy, et al., Deepid-net: Deformable deep convolutional neural networks for object detection, in: CVPR, 2015
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J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: CVPR, 2016
2016
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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, Ssd: Single shot multibox detector, in: ECCV, 2016
2016
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J. Hosang, R. Benenson, P. Dollár, B. Schiele, What makes for effective detection proposals?, TPAMI 38 (4) (2016) 814–830
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L. Shen, Z. Lin, Q. Huang, Relay backpropagation for e ective learning of deep convolutional neural networks, in: ECCV, 2016
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A. Shrivastava, A. Gupta, R. Girshick, Training region-based object detectors with online hard example mining, in: CVPR, 2016
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J. Dai, Y. Li, K. He, J. Sun, R-fcn: Object detection via region-based fully convolutional networks, in: NIPS, 2016
2016
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S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He, Aggregated residual transformations for deep neural networks, in: CVPR, 2017
2017
Closest in time.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, S. Belongie, Feature pyramid networks for object detection, in: CVPR, 2017
2017
Closest in time.
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, K. Murphy, Speed/accuracy trade-offs for modern convolutional object detectors, in: CVPR, 2017
2017
Closest in time.