no code implementations • 30 Dec 2023 • Yuming Huang, Yingpin Chen, Changhui Wu, Hanrong Xie, Binhui Song, Hui Wang
In the local feature aggregation stage, we introduce a shift convolution to realize the interaction between local spatial information and channel information.
1 code implementation • 28 Jun 2023 • Yuming Huang, Bin Ren
In our paper, we design a robust framework called Robust Model-based Hindsight Experience Replay (RoMo-HER) which can effectively utilize the dynamical model in robot manipulation environments to enhance the sample efficiency.
no code implementations • 23 May 2023 • Yuming Huang, Yi Gu, Chengzhong Xu, Hui Kong
Specifically, semantic segmentation is achieved by a new mask-range transformer network in a mask-classfication paradigm.
no code implementations • 24 Jun 2022 • Yi Gu, Yuming Huang, Chengzhong Xu, Hui Kong
To answer this question, we propose a unified mask-classification model, MaskRange, for the range-view based LiDAR semantic and panoptic segmentation.
no code implementations • 16 Nov 2021 • Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, Guanglu Song, Yichao Wu, Yuming Huang, Fenggang Liu, Huan Peng, Shuo Qin, Chengyu Wang, Yujie Wang, Conghui He, Ding Liang, Yu Liu, Fengwei Yu, Junjie Yan, Dahua Lin, Xiaogang Wang, Yu Qiao
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
no code implementations • 18 Oct 2021 • Yingpin Chen, Yuming Huang, Lingzhi Wang, Huiying Huang, Jianhua Song, Chaoqun Yu, Yanping Xu
For example, the noise location information is often ignored and the sparsity of the salt and pepper noise is often described by L1 norm, which cannot illustrate the sparse variables clearly.
no code implementations • 23 Sep 2019 • Yuming Huang, Ashkan Panahi, Hamid Krim, Liyi Dai
We further demonstrate by numerical experiments that in the presence of consistent communities between different layers, consistent communities are matched, and the detectability is improved over a single layer.
no code implementations • 13 Sep 2019 • Yuming Huang, Ashkan Panahi, Hamid Krim, Yiyi Yu, Spencer L. Smith
We present a novel adversarial framework for training deep belief networks (DBNs), which includes replacing the generator network in the methodology of generative adversarial networks (GANs) with a DBN and developing a highly parallelizable numerical algorithm for training the resulting architecture in a stochastic manner.