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Accurate and Efficient Similarity Search for Large Scale Face Recognition

Autor: Qi, Ce, Liu, Zhizhong, Su, Fei

Face verification is a relatively easy task with the help of discriminative features from deep neural networks. However, it is still a challenge to recognize faces on millions of identities while keeping high performance and efficiency. The challenge

Externí odkaz: http://arxiv.org/abs/1806.00365

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Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection

Autor: Qi, Ce, Chen, Xiaoping, Wang, Pingyu, Su, Fei

For the training of face detection network based on R-CNN framework, anchors are assigned to be positive samples if intersection-over-unions (IoUs) with ground-truth are higher than the first threshold(such as 0.7); and to be negative samples if thei

Externí odkaz: http://arxiv.org/abs/1804.10743

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Contrastive-center loss for deep neural networks

Autor: Qi, Ce, Su, Fei

The deep convolutional neural network(CNN) has significantly raised the performance of image classification and face recognition. Softmax is usually used as supervision, but it only penalizes the classification loss. In this paper, we propose a novel

Externí odkaz: http://arxiv.org/abs/1707.07391

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