no code implementations • 13 Mar 2023 • Jie Hu, Mengze Zeng, Enhua Wu
To bridge this gap, we collect and improve existing quantization methods and propose a gold guideline for post-training quantization.
1 code implementation • 17 Feb 2023 • Yangyuxuan Kang, Yuyang Liu, Anbang Yao, Shandong Wang, Enhua Wu
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.
no code implementations • 20 Dec 2022 • Ying Nie, Kai Han, Haikang Diao, Chuanjian Liu, Enhua Wu, Yunhe Wang
To this end, we first thoroughly analyze the difference on distributions of weights and activations in AdderNet and then propose a new quantization algorithm by redistributing the weights and the activations.
1 code implementation • 30 Sep 2022 • Dengsheng Chen, Jie Hu, Wenwen Qiang, Xiaoming Wei, Enhua Wu
In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain.
7 code implementations • 1 Jun 2022 • Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, Enhua Wu
In this paper, we propose to represent the image as a graph structure and introduce a new Vision GNN (ViG) architecture to extract graph-level feature for visual tasks.
Ranked #319 on Image Classification on ImageNet
2 code implementations • 10 Jan 2022 • Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chunjing Xu, Enhua Wu, Qi Tian
The proposed C-Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks.
no code implementations • 19 Dec 2021 • Jie Hu, Ziheng Wu, Vince Tan, Zhilin Lu, Mengze Zeng, Enhua Wu
For example, we raise the top-1 accuracy of binarized ResNet26 from 57. 9% to 64. 0%.
no code implementations • 20 Sep 2021 • Kai Han, Yunhe Wang, Chang Xu, Chunjing Xu, Enhua Wu, DaCheng Tao
A series of secondary filters can be derived from a primary filter with the help of binary masks.
1 code implementation • ICCV 2021 • Jiaming Liu, Ming Lu, Kaixin Chen, Xiaoqi Li, Shizun Wang, Zhaoqing Wang, Enhua Wu, Yurong Chen, Chuang Zhang, Ming Wu
Internet video delivery has undergone a tremendous explosion of growth over the past few years.
3 code implementations • NeurIPS 2021 • Mingjian Zhu, Kai Han, Enhua Wu, Qiulin Zhang, Ying Nie, Zhenzhong Lan, Yunhe Wang
To this end, we propose a novel dynamic-resolution network (DRNet) in which the input resolution is determined dynamically based on each input sample.
10 code implementations • NeurIPS 2021 • Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang
In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT).
1 code implementation • ICML 2020 • Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu
This paper formalizes the binarization operations over neural networks from a learning perspective.
72 code implementations • CVPR 2018 • Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu
Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2. 251%, surpassing the winning entry of 2016 by a relative improvement of ~25%.
Ranked #59 on Image Classification on CIFAR-10
no code implementations • CVPR 2015 • Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu
Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR).