2 code implementations • 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 • 5 Sep 2023 • Mengyao Guo, Xiaolin Zhang, Yuan Zhuang, Jing Chen, Pengfei Wang, Ze Gao
This paper explores using generative AI and aesthetics to promote cultural creativity in rural China amidst COVID-19's impact.
no code implementations • 10 Aug 2023 • Qin Zhang, Zelin Shi, Xiaolin Zhang, Xiaojun Chen, Philippe Fournier-Viger, Shirui Pan
Node classification is the task of predicting the labels of unlabeled nodes in a graph.
1 code implementation • 26 Jul 2023 • Yixuan Ma, Xiaolin Zhang, Peng Zhang, Kun Zhan
In this paper, we theoretically illustrate that the entropy of a dataset can be approximated by maximizing the lower bound of the mutual information across different views of a graph, \ie, entropy is estimated by a neural network.
1 code implementation • 26 Jul 2023 • Zhibo Tain, Xiaolin Zhang, Peng Zhang, Kun Zhan
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples.
no code implementations • 19 Feb 2023 • Haixu Long, Xiaolin Zhang, Yanbin Liu, Zongtai Luo, Jianbo Liu
In this paper, we try to look into the root cause of the LTR task, i. e., training samples for each class are greatly imbalanced, and propose a straightforward solution.
no code implementations • 11 Jan 2022 • Zipeng Qin, Jianbo Liu, Xiaolin Zhang, Maoqing Tian, Aojun Zhou, Shuai Yi, Hongsheng Li
The recently proposed MaskFormer gives a refreshed perspective on the task of semantic segmentation: it shifts from the popular pixel-level classification paradigm to a mask-level classification method.
1 code implementation • ECCV 2020 • Xiaolin Zhang, Yunchao Wei, Yi Yang
We learn a feature center for each category and realize the global feature consistency by forcing the object features to approach class-specific centers.
no code implementations • 25 Jun 2020 • Jingang Tan, Lili Chen, Kangru Wang, Jingquan Peng, Jiamao Li, Xiaolin Zhang
We propose a novel 3D point cloud segmentation framework named SASO, which jointly performs semantic and instance segmentation tasks.
Ranked #2 on 3D Instance Segmentation on S3DIS (mIoU metric)
3D Instance Segmentation 3D Semantic Instance Segmentation +4
1 code implementation • 9 Jun 2020 • Xiaolin Zhang, Yunchao Wei, Yi Yang, Fei Wu
To fulfill the direct evaluation, we annotate pixel-level object masks on the ILSVRC validation set.
no code implementations • 1 Mar 2020 • Liang Du, Jingang Tan, xiangyang xue, Lili Chen, Hongkai Wen, Jianfeng Feng, Jiamao Li, Xiaolin Zhang
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation.
no code implementations • IEEE Access ( Volume: 8 ) 2020 • Hongye Yang, Yuzhang Gu, Jianchao Zhu, Keli Hu, Xiaolin Zhang
In addition, such a fixed adjacency matrix used in all layers leads to the network failing to extract multi-level semantic features.
Ranked #59 on Skeleton Based Action Recognition on NTU RGB+D
1 code implementation • 22 Oct 2018 • Xiaolin Zhang, Yunchao Wei, Yi Yang, Thomas Huang
In this way, the possibilities embedded in the produced similarity maps can be adapted to guide the process of segmenting objects.
Ranked #89 on Few-Shot Semantic Segmentation on PASCAL-5i (5-Shot)
no code implementations • ECCV 2018 • Xiaoqing Ye, Jiamao Li, Hexiao Huang, Liang Du, Xiaolin Zhang
Semantic segmentation of 3D unstructured point clouds remains an open research problem.
1 code implementation • ECCV 2018 • Xiaolin Zhang, Yunchao Wei, Guoliang Kang, Yi Yang, Thomas Huang
A stagewise approach is proposed to incorporate high confident object regions to learn the SPG masks.
Ranked #1 on Weakly-Supervised Object Localization on ILSVRC 2015
2 code implementations • CVPR 2018 • Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, Thomas Huang
With such an adversarial learning, the two parallel-classifiers are forced to leverage complementary object regions for classification and can finally generate integral object localization together.
Ranked #2 on Weakly-Supervised Object Localization on ILSVRC 2016
no code implementations • 27 Sep 2016 • Kangru Wang, Lei Qu, Lili Chen, Yuzhang Gu, DongChen zhu, Xiaolin Zhang
The main contribution of this paper is a newly proposed descriptor which is implemented in the disparity image to obtain a disparity texture image.