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We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Selective search for object recognition
Uijlings, J. R., van de Sande, K. E., Gevers, T., and Smeulders, A. W. (2013) · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014) · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2014) · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014) · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
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Edge boxes: Locating object proposals from edges
Zitnick, C. L. and Dollár, P. (2014) · 2014
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The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. (2015) · 2015
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Girshick, R. (2015) · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J. (2015) · 2015
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Bell, S., Lawrence Zitnick, C., Bala, K., and Girshick, R. (2016) · 2016
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A unified multi-scale deep convolutional neural network for fast object detection
Cai, Z., Fan, Q., Feris, R. S., and Vasconcelos, N. (2016) · 2016
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R-fcn: Object detection via region-based fully convolutional networks
Dai, J., Li, Y., He, K., and Sun, J. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Feature pyramid networks for object detection
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2016) · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016) · 2016
Cited alongside, same era.
Stacked hourglass networks for human pose estimation
Newell, A., Yang, K., and Deng, J. (2016) · 2016
Cited alongside, same era.
Speed/accuracy trade-offs for modern convolutional object detectors
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al. (2017) · 2017
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Ron: Reverse connection with objectness prior networks for object detection
Kong, T., Sun, F., Yao, A., Liu, H., Lu, M., and Chen, Y. (2017) · 2017
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Light-head r-cnn: In defense of two-stage object detector
Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., and Sun, J. (2017) · 2017
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017) · 2017
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Pixels to graphs by associative embedding
Newell, A. and Deng, J. (2017) · 2017
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Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016) · 2016
Cited alongside, same era.
Yolo9000: better, faster, stronger
Redmon, J. and Farhadi, A. (2016) · 2016
Cited alongside, same era.
Beyond skip connections: Top-down modulation for object detection
Shrivastava, A., Sukthankar, R., Malik, J., and Gupta, A. (2016) · 2016
Cited alongside, same era.
Subcategory-aware convolutional neural networks for object proposals and detection
Xiang, Y., Choi, W., Lin, Y., and Savarese, S. (2016) · 2016
Cited alongside, same era.
Soft-nms—improving object detection with one line of code
Bodla, N., Singh, B., Chellappa, R., and Davis, L. S. (2017) · 2017
Cited alongside, same era.
Cascade r-cnn: Delving into high quality object detection
Cai, Z. and Vasconcelos, N. (2017) · 2017
Cited alongside, same era.
Dual path networks
Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., and Feng, J. (2017) · 2017
Cited alongside, same era.
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Associative embedding: End-to-end learning for joint detection and grouping
Newell, A., Huang, Z., and Deng, J. (2017) · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017) · 2017
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An analysis of scale invariance in object detection-snip
Singh, B. and Davis, L. S. (2017) · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A. (2017) · 2017
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Point linking network for object detection
Wang, X., Chen, K., Huang, Z., Yao, C., and Liu, W. (2017) · 2017
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Deep regionlets for object detection
Xu, H., Lv, X., Wang, X., Ren, Z., and Chellappa, R. (2017) · 2017
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Feature selective networks for object detection
Zhai, Y., Fu, J., Lu, Y., and Li, H. (2017) · 2017
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Single-shot refinement neural network for object detection
Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S. Z. (2017) · 2017
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Couplenet: Coupling global structure with local parts for object detection
Zhu, Y., Zhao, C., Wang, J., Zhao, X., Wu, Y., and Lu, H. (2017) · 2017
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Acquisition of localization confidence for accurate object detection
Jiang, B., Luo, R., Mao, J., Xiao, T., and Jiang, Y. (2018) · 2018
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