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ParameterNet: Parameters Are All You Need
Kai Han, Yunhe Wang, Jianyuan Guo +1
The large-scale visual pretraining has significantly improve the performance of large vision models. However, we observe the \emph{low FLOPs pitfall} that the existing low-FLOPs mo…
Robust and Efficient Memory Network for Video Object Segmentation
Yadang Chen, Dingwei Zhang, Zhi-xin Yang +1
This paper proposes a Robust and Efficient Memory Network, referred to as REMN, for studying semi-supervised video object segmentation (VOS). Memory-based methods have recently ach…
Bag of Tricks with Quantized Convolutional Neural Networks for image classification
Jie Hu, Mengze Zeng, Enhua Wu
Deep neural networks have been proven effective in a wide range of tasks. However, their high computational and memory costs make them impractical to deploy on resource-constrained…
3D Human Pose Lifting with Grid Convolution
Yangyuxuan Kang, Yuyang Liu, Anbang Yao +2
Existing lifting networks for regressing 3D human poses from 2D single-view poses are typically constructed with linear layers based on graph-structured representation learning. In…
Redistribution of Weights and Activations for AdderNet Quantization
Ying Nie, Kai Han, Haikang Diao +3
Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions…
Vision GNN: An Image is Worth Graph of Nodes
Kai Han, Yunhe Wang, Jianyuan Guo +2
Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or s…