1k citations · 1.5k across the 16 of their papers we have counts for
12 papers · 1 filter
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…
GhostNets on Heterogeneous Devices via Cheap Operations
Kai Han, Yunhe Wang, Chang Xu +4
Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the limited memory and computation resources. We aim to design efficient neural networks for he…
Elastic-Link for Binarized Neural Network
Jie Hu, Ziheng Wu, Vince Tan +3
Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrai…
Learning Versatile Convolution Filters for Efficient Visual Recognition
Kai Han, Yunhe Wang, Chang Xu +3
This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of ef…
Dynamic Resolution Network
Mingjian Zhu, Kai Han, Enhua Wu +4
Deep convolutional neural networks (CNNs) are often of sophisticated design with numerous learnable parameters for the accuracy reason. To alleviate the expensive costs of deployin…
Transformer in Transformer
Kai Han, An Xiao, Enhua Wu +3
Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the…
Training Binary Neural Networks through Learning with Noisy Supervision
Kai Han, Yunhe Wang, Yixing Xu +3
This paper formalizes the binarization operations over neural networks from a learning perspective. In contrast to classical hand crafted rules (\eg hard thresholding) to binarize…