1 code implementation • 16 Apr 2024 • Bin Ren, Nancy Mehta, Radu Timofte, Hongyuan Yu, Cheng Wan, Yuxin Hong, Bingnan Han, Zhuoyuan Wu, Yajun Zou, Yuqing Liu, Jizhe Li, Keji He, Chao Fan, Heng Zhang, Xiaolin Zhang, Xuanwu Yin, Kunlong Zuo, Bohao Liao, Peizhe Xia, Long Peng, Zhibo Du, Xin Di, Wangkai Li, Yang Wang, Wei Zhai, Renjing Pei, Jiaming Guo, Songcen Xu, Yang Cao, ZhengJun Zha, Yan Wang, Yi Liu, Qing Wang, Gang Zhang, Liou Zhang, Shijie Zhao, Long Sun, Jinshan Pan, Jiangxin Dong, Jinhui Tang, Xin Liu, Min Yan, Menghan Zhou, Yiqiang Yan, Yixuan Liu, Wensong Chan, Dehua Tang, Dong Zhou, Li Wang, Lu Tian, Barsoum Emad, Bohan Jia, Junbo Qiao, Yunshuai Zhou, Yun Zhang, Wei Li, Shaohui Lin, Shenglong Zhou, Binbin Chen, Jincheng Liao, Suiyi Zhao, Zhao Zhang, Bo wang, Yan Luo, Yanyan Wei, Feng Li, Mingshen Wang, Yawei Li, Jinhan Guan, Dehua Hu, Jiawei Yu, Qisheng Xu, Tao Sun, Long Lan, Kele Xu, Xin Lin, Jingtong Yue, Lehan Yang, Shiyi Du, Lu Qi, Chao Ren, Zeyu Han, YuHan Wang, Chaolin Chen, Haobo Li, Mingjun Zheng, Zhongbao Yang, Lianhong Song, Xingzhuo Yan, Minghan Fu, Jingyi Zhang, Baiang Li, Qi Zhu, Xiaogang Xu, Dan Guo, Chunle Guo, Jiadi Chen, Huanhuan Long, Chunjiang Duanmu, Xiaoyan Lei, Jie Liu, Weilin Jia, Weifeng Cao, Wenlong Zhang, Yanyu Mao, Ruilong Guo, Nihao Zhang, Qian Wang, Manoj Pandey, Maksym Chernozhukov, Giang Le, Shuli Cheng, Hongyuan Wang, Ziyan Wei, Qingting Tang, Liejun Wang, Yongming Li, Yanhui Guo, Hao Xu, Akram Khatami-Rizi, Ahmad Mahmoudi-Aznaveh, Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou, Amogh Joshi, Nikhil Akalwadi, Sampada Malagi, Palani Yashaswini, Chaitra Desai, Ramesh Ashok Tabib, Ujwala Patil, Uma Mudenagudi
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
no code implementations • 15 Oct 2023 • Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL).
1 code implementation • 6 Oct 2023 • Yinda Chen, Wei Huang, Shenglong Zhou, Qi Chen, Zhiwei Xiong
By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 31 Aug 2023 • Shenglong Zhou, Kaidi Xu, Geoffrey Ye Li
Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process.
1 code implementation • ICCV 2023 • Mingde Yao, Jie Huang, Xin Jin, Ruikang Xu, Shenglong Zhou, Man Zhou, Zhiwei Xiong
Existing methods typically work well on their trained lightness conditions but perform poorly in unknown ones due to their limited generalization ability.
1 code implementation • ICCV 2023 • Rui Li, Shenglong Zhou, Dong Liu
We address the problem of learning features for establishing pixel-wise correspondences.
1 code implementation • ICCV 2023 • Xiaoyu Liu, Wei Huang, Zhiwei Xiong, Shenglong Zhou, Yueyi Zhang, Xuejin Chen, Zheng-Jun Zha, Feng Wu
Sparse instance-level supervision has recently been explored to address insufficient annotation in biomedical instance segmentation, which is easier to annotate crowded instances and better preserves instance completeness for 3D volumetric datasets compared to common semi-supervision. In this paper, we propose a sparsely supervised biomedical instance segmentation framework via cross-representation affinity consistency regularization.
1 code implementation • 23 Aug 2022 • Shenglong Zhou, and Geoffrey Ye Li
Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics.
no code implementations • 19 Jun 2022 • HUI ZHANG, Shenglong Zhou, Geoffrey Ye Li, Naihua Xiu
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs).
no code implementations • 20 May 2022 • Tao Yang, Shenglong Zhou, Yuwang Wang, Yan Lu, Nanning Zheng
Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift.
1 code implementation • 3 May 2022 • Shenglong Zhou, Geoffrey Ye Li
Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge.
1 code implementation • 22 Apr 2022 • Shenglong Zhou, Geoffrey Ye Li
One of the crucial issues in federated learning is how to develop efficient optimization algorithms.
1 code implementation • 3 Feb 2022 • Mingxing Li, Shenglong Zhou, Chang Chen, Yueyi Zhang, Dong Liu, Zhiwei Xiong
Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast.
no code implementations • 21 Nov 2021 • Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
In this paper, we propose a new learning method named Accretionary Learning (AL) to emulate human learning, in that the set of objects to be recognized may not be pre-specified.
1 code implementation • 28 Oct 2021 • Shenglong Zhou, Geoffrey Ye Li
Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge.
no code implementations • 16 Dec 2019 • Huajun Wang, Yuan-Hai Shao, Shenglong Zhou, Ce Zhang, Naihua Xiu
To distinguish all of them, in this paper, we introduce a new model equipped with an $L_{0/1}$ soft-margin loss (dubbed as $L_{0/1}$-SVM) which well captures the nature of the binary classification.
1 code implementation • 9 Jan 2019 • Shenglong Zhou, Naihua Xiu, Hou-Duo Qi
Algorithms based on the hard thresholding principle have been well studied with sounding theoretical guarantees in the compressed sensing and more general sparsity-constrained optimization.
Optimization and Control
no code implementations • 17 Jul 2014 • Shenglong Zhou, Naihua Xiu, Ziyan Luo, Lingchen Kong
This paper aims at achieving a simultaneously sparse and low-rank estimator from the semidefinite population covariance matrices.