1 code implementation • 30 Aug 2024 • Chiu-Chou Lin, Yu-Wei Shih, Kuei-Ting Kuo, Yu-cheng Chen, Chien-Hua Chen, Wei-Chen Chiu, I-Chen Wu
The accuracy of the observed strength relations in these games is comparable to traditional pairwise win value predictions, while also offering a more manageable complexity for analysis.
no code implementations • 16 Feb 2024 • Tanner Fiez, Houssam Nassif, Yu-cheng Chen, Sergio Gamez, Lalit Jain
Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods.
no code implementations • 21 Aug 2020 • Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Yang Li, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-Wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Xiangjun Feng, Zhihong Liu, Daiqiang Li, Peter J. Schüffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
no code implementations • 2 Jun 2020 • Yu-cheng Chen, YingLi Tian, Mingyi He
Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences.
no code implementations • 28 May 2020 • Longlong Jing, Yu-cheng Chen, Ling Zhang, Mingyi He, YingLi Tian
By exploring the inherent multi-modality attributes of 3D objects, in this paper, we propose to jointly learn modal-invariant and view-invariant features from different modalities including image, point cloud, and mesh with heterogeneous networks for 3D data.
no code implementations • 13 Apr 2020 • Longlong Jing, Yu-cheng Chen, Ling Zhang, Mingyi He, YingLi Tian
Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network.
no code implementations • 8 Jun 2019 • Yu-cheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng
This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs).
no code implementations • 28 Dec 2018 • Longlong Jing, Yu-cheng Chen, YingLi Tian
The enhanced coarse mask is fed to a fully convolutional neural network to be recursively refined.
no code implementations • 19 Apr 2017 • Bo Li, Huahui Chen, Yu-cheng Chen, Yuchao Dai, Mingyi He
However, due to the difficulty in representing the 3D skeleton video and the lack of training data, action detection from streaming 3D skeleton video still lags far behind its recognition counterpart and image based object detection.
no code implementations • 19 Apr 2017 • Bo Li, Mingyi He, Xuelian Cheng, Yu-cheng Chen, Yuchao Dai
Especially on the largest and challenge NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods by a large margion, which proves the efficacy of the proposed method.
Ranked #84 on Skeleton Based Action Recognition on NTU RGB+D