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

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書目名稱Computer Vision – ECCV 2020影響因子(影響力)




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Semi-Siamese Training for Shallow Face Learning,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

出生

斥責

宏偉

宏偉

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation,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

Dorsal

Adaptive Computationally Efficient Network for Monocular 3D Hand Pose Estimation,nced algorithms to achieve high pose estimation accuracy. However, besides accuracy, the computation efficiency that affects the computation speed and power consumption is also crucial for real-world applications. In this paper, we investigate the problem of reducing the overall computation cost yet

Ganglion

euphoria

Distribution-Balanced Loss for Multi-label Classification in Long-Tailed Datasets,. Compared to conventional single-label classification problem, multi-label recognition problems are often more challenging due to two significant issues, namely the co-occurrence of labels and the dominance of negative labels (when treated as multiple binary classification problems). The Distributi