1 code implementation • Computers and Electrical Engineering 2023 • Xiwang Xie, Xipeng Pan, Feng Shao, Weidong Zhang, Jubai An
Owing to the various object scales and high similarity with the surrounding organs (e. g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images.
no code implementations • 20 Apr 2023 • Chenglu Sun, Yichi Zhang, Yu Zhang, Ziling Lu, Jingbin Liu, Sijia Xu, Weidong Zhang
We propose asymmetric-evolution training (AET), a novel multi-agent reinforcement learning framework that can train multiple kinds of agents simultaneously in AMP game.
1 code implementation • CVPR 2023 • Qi Fang, Kang Chen, Yinghui Fan, Qing Shuai, Jiefeng Li, Weidong Zhang
Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks.
1 code implementation • 5 Dec 2022 • Yiren Song, Xuning Shao, Kang Chen, Weidong Zhang, Minzhe Li, Zhongliang Jing
Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation.
1 code implementation • 14 Aug 2022 • Chunle Guo, Ruiqi Wu, Xin Jin, Linghao Han, Zhi Chai, Weidong Zhang, Chongyi Li
To achieve that, we also contribute a dataset, URankerSet, containing sufficient results enhanced by different UIE algorithms and the corresponding perceptual rankings, to train our URanker.
no code implementations • 15 Dec 2021 • Xiqi Yang, Kewei Yang, Kang Chen, Weidong Zhang, Weiwei Xu
The guidance of the rendered image is realized by blending features from two branches effectively in the training of the detail branch, which improves both the warping accuracy and the details' fidelity.
1 code implementation • ICCV 2021 • Xuning Shao, Weidong Zhang
For unsupervised image-to-image translation, we propose a discriminator architecture which focuses on the statistical features instead of individual patches.
no code implementations • 1 Jan 2021 • Gege Zhang, Gangwei Li, Weining Shen, Huixin Zhang, Weidong Zhang
Expressivity plays a fundamental role in evaluating deep neural networks, and it is closely related to understanding the limit of performance improvement.
no code implementations • 29 Dec 2020 • Xiu-Shen Wei, Yu-Yan Xu, Yazhou Yao, Jia Wei, Si Xi, Wenyuan Xu, Weidong Zhang, Xiaoxin Lv, Dengpan Fu, Qing Li, Baoying Chen, Haojie Guo, Taolue Xue, Haipeng Jing, Zhiheng Wang, Tianming Zhang, Mingwen Zhang
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc.
no code implementations • 15 Sep 2020 • Yubo Huang, Xuechun Wang, Luobao Zou, Zhiwei Zhuang, Weidong Zhang
In reinforcement learning (RL), we always expect the agent to explore as many states as possible in the initial stage of training and exploit the explored information in the subsequent stage to discover the most returnable trajectory.
no code implementations • ECCV 2020 • Weidong Zhang, Wei zhang, yinda zhang
The task of room layout estimation is to locate the wall-floor, wall-ceiling, and wall-wall boundaries.
1 code implementation • 4 Aug 2020 • Mengzuo Huang, Feng Li, Wuhe Zou, Weidong Zhang
Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission.
Ranked #1 on Dialogue Rewriting on Multi-Rewrite (BLEU-1 metric)
no code implementations • 29 Oct 2019 • Rui Fan, Yu-An Wang, Lei Qiao, Ruiwen Yao, Peng Han, Weidong Zhang, Ioannis Pitas, Ming Liu
This linear model is then utilized to reduce the redundant information in the left and right road images.
no code implementations • 11 Oct 2019 • Gege Zhang, Gangwei Li, Ningwei Shen, Weidong Zhang
In this paper, we provide a quantitative analysis of the expressivity for the deep neural network (DNN) from its dynamic model, where the Hilbert space is employed to analyze the convergence and criticality.
no code implementations • 3 Jan 2019 • Weidong Zhang, Wei zhang, Jason Gu
More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image.