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pro vyhledávání: '"Fang, Linpu"'
Binary neural networks (BNNs), where both weights and activations are binarized into 1 bit, have been widely studied in recent years due to its great benefit of highly accelerated computation and substantially reduced memory footprint that appeal to
Externí odkaz:
http://arxiv.org/abs/2010.09294
State-of-the-art single depth image-based 3D hand pose estimation methods are based on dense predictions, including voxel-to-voxel predictions, point-to-point regression, and pixel-wise estimations. Despite the good performance, those methods have a
Externí odkaz:
http://arxiv.org/abs/2007.04646
Replacing normal convolutions with group convolutions can significantly increase the computational efficiency of modern deep convolutional networks, which has been widely adopted in compact network architecture designs. However, existing group convol
Externí odkaz:
http://arxiv.org/abs/2007.04242
The dominant object detection approaches treat each dataset separately and fit towards a specific domain, which cannot adapt to other domains without extensive retraining. In this paper, we address the problem of designing a universal object detectio
Externí odkaz:
http://arxiv.org/abs/2002.07417
Object detectors trained on fully-annotated data currently yield state of the art performance but require expensive manual annotations. On the other hand, weakly-supervised detectors have much lower performance and cannot be used reliably in a realis
Externí odkaz:
http://arxiv.org/abs/2002.07421
Publikováno v:
In Signal Processing: Image Communication March 2020 82
Autor:
Fang, Linpu1 (AUTHOR), Wu, Guile1 (AUTHOR), Kang, Wenxiong1 (AUTHOR) auwxkang@scut.edu.cn, Wu, Qiuxia2 (AUTHOR), Wang, Zhiyong3 (AUTHOR), Feng, David Dagan3 (AUTHOR)
Publikováno v:
Neural Computing & Applications. Dec2019, Vol. 31 Issue 12, p8533-8546. 14p.
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