[업데이트 2018.09.08 17:41] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) Traffic sign detection and recognition has been a popular problem in intelligent vehicles, and various methods [20, 15, 34, 19, 38, 45] have been pro- posed to address this challenging task. Traditional methods for this task includes [20] [15]. 2. 레퍼런스 논..
[업데이트 2018.09.08 17:19] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) Jin et al. [19] proposed to train the CNN with hingle loss, which provides better test accuracy and faster stable convergence. 2. 레퍼런스 논문[19] J. Jin, K. Fu, and C. Zhang. Traffic sign recognition with hinge loss trained convolutional neural networks. IEE..
[업데이트 2018.09.08 17:00] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) Some efforts [4, 25, 18, 39, 23, 1] have been devoted to addressing small object detection problems. One common practice [4, 25] is to increase the scale of input images to enhance the resolution of small objects and produce high-resolution feature maps...
[업데이트 2018.09.08 16:28] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) The implementation is based on the publicly available Fast R-CNN framework [11] built on the Caffe platform [17]. 2. 레퍼런스 논문[17] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional archi..
[업데이트 2018.09.08 16:28] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) Following [16], we perform down-sampling directly by convolutional layers with a stride of 2. 2. 레퍼런스 논문[16] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015. 5(https://arxiv.org/pdf..
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