Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

知道

https://doi.org/10.1007/978-3-030-58548-8computer vision; correlation analysis; data security; databases; face recognition; Human-Computer Interac

幻想

978-3-030-58547-1Springer Nature Switzerland AG 2020

緊張過度

去世

The Return of the Reserve Army,thods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue that even if each domain contains a single image, UI2I can still be achieved. To this end, we propose TuiGAN, a generative model

削減

The Elements of Economic Theory,fficient number of samples) for training. However, in many real-world scenarios of face recognition, the training dataset is limited in depth, . only two face images are available for each ID. . Unlike deep face data, the shallow face data lacks intra-class diversity. As such, it can lead to collaps

古代

https://doi.org/10.1007/978-1-349-81732-0 resource-constrained mobile devices. Similar to other deep models, state-of-the-art GANs suffer from high parameter complexities. That has recently motivated the exploration of compressing GANs (usually generators). Compared to the vast literature and prevailing success in compressing deep classifi

HPA533

https://doi.org/10.1007/978-1-349-81732-0ints. Unlike previous work, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these 3D skeleton point clouds. A novel .keleton .oints .nteraction .earning (SPIL) module, is proposed to model the interactions betwe

dilute

The Life and Work of Karl Polanyi, be applied in real-world applications due to the heavy computation requirement. Model quantization is an effective way to significantly reduce model size and computation time. In this work, we investigate the binary neural network-based SISR problem and propose a novel model binarization method. Sp

不安

The Life and Work of Karl Polanyi,nteractions. Recent works prove it possible to stack self-attention layers to obtain a fully attentional network by restricting the attention to a local region. In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions. This reduces computation

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