no code implementations • 29 Mar 2024 • Haikuo Shao, Huihong Shi, Wendong Mao, Zhongfeng Wang
Vision Transformers (ViTs) have achieved significant success in computer vision.
no code implementations • 17 Dec 2023 • Siyu Zhang, Wendong Mao, Huihong Shi, Zhongfeng Wang
Video compression is widely used in digital television, surveillance systems, and virtual reality.
1 code implementation • 16 Aug 2023 • Minghao She, Wendong Mao, Huihong Shi, Zhongfeng Wang
In this paper, we propose a double-win framework for ideal and blind SR task, named S2R, including a light-weight transformer-based SR model (S2R transformer) and a novel coarse-to-fine training strategy, which can achieve excellent visual results on both ideal and random fuzzy conditions.
no code implementations • 4 Nov 2022 • Mingyu Zhu, Jiapeng Luo, Wendong Mao, Zhongfeng Wang
In this paper, an efficient hardware accelerator is proposed for deep forest models, which is also the first work to implement Deep Forest on FPGA.
no code implementations • 18 Oct 2022 • Ziqi Su, Wendong Mao, Zhongfeng Wang, Jun Lin, WenQiang Wang, Haitao Sun
3D deconvolution (DeConv), as an important computation of 3D-GAN, significantly increases computational complexity compared with 2D DeConv.
no code implementations • 14 Oct 2022 • Zilun Wang, Wendong Mao, Peixiang Yang, Zhongfeng Wang, Jun Lin
The submanifold sparse convolutional network (SSCN) has been widely used for the point cloud due to its unique advantages in terms of visual results.
no code implementations • 2 Oct 2018 • Wendong Mao, Mingjie Wang, Jun Zhou, Minglun Gong
A robust solution for semi-dense stereo matching is presented.
no code implementations • 2 Oct 2018 • Mingjie Wang, Jun Zhou, Wendong Mao, Minglun Gong
To address this problem, a regularization method named Stochastic Feature Reuse is also presented.
2 code implementations • 22 Mar 2018 • Zili Yi, Zhiqin Chen, Hao Cai, Wendong Mao, Minglun Gong, Hao Zhang
The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features.