[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..
[업데이트 2018.09.07 16:22] 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 proposed to address this challenging task. Traditional methods for this task includes [20] [15]. 2. 레퍼런스 논문[..
[업데이트 2018.09.07 16:03] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) The Generative Adversarial Networks (GANs) [14] is a framework for learning generative models. The learning objective for vanilla GAN models [14] corresponds to a minimax two-player game, which is formulated as 2. 레퍼런스 논문[14] I. Goodfellow, J. Pouget-Aba..
[업데이트 2018.09.07 11:45] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) For the generator and the discriminator network, the parameters of newly added convolutional layers and fully connected layers are initialized with “Xavier” [13]. 2. 레퍼런스 논문[13] X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedf..
[업데이트 2018.09.07 10:15] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) 2. 레퍼런스 논문[12] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, pages 580–587, 2014. 5(https://arxiv.org/pdf/1311.2524.pdf) 3. 주요 내용 요약3.1 레퍼런스 논문 주요 내용R-CNN에 대한..
[업데이트 2018.09.07 10:15] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) Recent great progress on object detection is stimulated by the deep learning pipelines that learn deep representations from the region of interest (RoI) and perform classification based on the learned representations, such as Fast R-CNN [11] and Faster R..
[업데이트 2018.09.07 10:15] 1. 베이스 논문[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection(http://arclab.tistory.com/172) To evaluate the generalization capability of the proposed generator on more general and diverse object categories, we train the proposed detection pipeline with the generator network end-to-end on the union of the trainval set of PASCAL VOC 2007 and VOC ..
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