1 code implementation • 12 Apr 2023 • Yifeng Shi, Feng Lv, Xinliang Wang, Chunlong Xia, Shaojie Li, Shujie Yang, Teng Xi, Gang Zhang
To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry.
1 code implementation • 17 Mar 2023 • Qiankun Gao, Chen Zhao, Yifan Sun, Teng Xi, Gang Zhang, Bernard Ghanem, Jian Zhang
The "pre-training $\rightarrow$ downstream adaptation" presents both new opportunities and challenges for Continual Learning (CL).
1 code implementation • 13 Feb 2023 • Gang Zhang, Ziyi Li, Jianmin Li, Xiaolin Hu
Multi-scale features are essential for dense prediction tasks, including object detection, instance segmentation, and semantic segmentation.
no code implementations • 11 Feb 2023 • ZhenLiang Ni, Fukui Yang, Shengzhao Wen, Gang Zhang
By distilling the global pixel relation, the student detector can learn the relation between foreground and background features, and avoid the difficulty of distilling features directly for the feature imbalance issue.
no code implementations • CVPR 2023 • Xiaoyan Li, Gang Zhang, Boyue Wang, Yongli Hu, BaoCai Yin
LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time.
1 code implementation • 15 Nov 2022 • Yu Wang, Xin Li, Shengzhao Wen, Fukui Yang, Wanping Zhang, Gang Zhang, Haocheng Feng, Junyu Han, Errui Ding
In this paper, we focus on the compression of DETR with knowledge distillation.
no code implementations • arXiv 2022 • Qiang Chen, Jian Wang, Chuchu Han, Shan Zhang, Zexian Li, Xiaokang Chen, Jiahui Chen, Xiaodi Wang, Shuming Han, Gang Zhang, Haocheng Feng, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang
The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO.
Ranked #5 on Object Detection on COCO test-dev
1 code implementation • 23 Jul 2022 • Chufeng Tang, Lingxi Xie, Gang Zhang, Xiaopeng Zhang, Qi Tian, Xiaolin Hu
In this paper, we present an economic active learning setting, named active pointly-supervised instance segmentation (APIS), which starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object.
1 code implementation • 21 Jul 2022 • Teng Xi, Yifan Sun, Deli Yu, Bi Li, Nan Peng, Gang Zhang, Xinyu Zhang, Zhigang Wang, Jinwen Chen, Jian Wang, Lufei Liu, Haocheng Feng, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang
UFO aims to benefit each single task with a large-scale pretraining on all tasks.
2 code implementations • 21 Apr 2022 • Xiaoyan Li, Gang Zhang, Hongyu Pan, Zhenhua Wang
LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms.
Ranked #12 on Robust 3D Semantic Segmentation on SemanticKITTI-C
1 code implementation • 22 Feb 2022 • Zhen Zhao, Yuqiu Liu, Gang Zhang, Liang Tang, Xiaolin Hu
This report introduces our solution to the iFLYTEK challenge 2021 cultivated land extraction from high-resolution remote sensing image.
1 code implementation • CVPR 2022 • Bo Li, Yongqiang Yao, Jingru Tan, Gang Zhang, Fengwei Yu, Jianwei Lu, Ye Luo
The conventional focal loss balances the training process with the same modulating factor for all categories, thus failing to handle the long-tailed problem.
1 code implementation • 19 Aug 2021 • Xiawu Zheng, Yuexiao Ma, Teng Xi, Gang Zhang, Errui Ding, Yuchao Li, Jie Chen, Yonghong Tian, Rongrong Ji
This practically limits the application of model compression when the model needs to be deployed on a wide range of devices.
1 code implementation • CVPR 2021 • Bi Li, Teng Xi, Gang Zhang, Haocheng Feng, Junyu Han, Jingtuo Liu, Errui Ding, Wenyu Liu
Since only a subset of classes is selected for each iteration, the computing requirement is reduced.
Ranked #4 on Face Recognition on AgeDB-30
1 code implementation • CVPR 2021 • Gang Zhang, Xin Lu, Jingru Tan, Jianmin Li, Zhaoxiang Zhang, Quanquan Li, Xiaolin Hu
In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner.
no code implementations • 22 Feb 2021 • Mingmin Zhong, Ying Liu, Feng Zhou, Minquan Kuang, Tie Yang, Xiaotian Wang, Gang Zhang
However, these materials are uncommon because these excitations in electronic systems are usually broken by spin-orbit coupling (SOC) and normally far from the Fermi level.
no code implementations • ICCV 2021 • Qinqin Zhou, Xiawu Zheng, Liujuan Cao, Bineng Zhong, Teng Xi, Gang Zhang, Errui Ding, Mingliang Xu, Rongrong Ji
EC-DARTS decouples different operations based on their categories to optimize the operation weights so that the operation gap between them is shrinked.
2 code implementations • CVPR 2021 • Jingru Tan, Xin Lu, Gang Zhang, Changqing Yin, Quanquan Li
To address the problem of imbalanced gradients, we introduce a new version of equalization loss, called equalization loss v2 (EQL v2), a novel gradient guided reweighing mechanism that re-balances the training process for each category independently and equally.
Ranked #9 on Instance Segmentation on LVIS v1.0 val
no code implementations • 22 Oct 2020 • Baopu Li, Yanwen Fan, Zhihong Pan, Gang Zhang
In the process of pruning, we utilize a searchable hyperparameter, remaining ratio, to denote the number of channels in each convolution layer, and then a dynamic masking process is proposed to describe the corresponding channel evolution.
no code implementations • 25 Sep 2020 • Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, WangMeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Tangxin Xie, Liang Cao, Yan Zou, Yi Shen, Jialiang Zhang, Yu Jia, Kaihua Cheng, Chenhuan Wu, Yue Lin, Cen Liu, Yunbo Peng, Xueyi Zou, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Tongtong Zhao, Shanshan Zhao, Yoseob Han, Byung-Hoon Kim, JaeHyun Baek, HaoNing Wu, Dejia Xu, Bo Zhou, Wei Guan, Xiaobo Li, Chen Ye, Hao Li, Yukai Shi, Zhijing Yang, Xiaojun Yang, Haoyu Zhong, Xin Li, Xin Jin, Yaojun Wu, Yingxue Pang, Sen Liu, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Marie-Paule Cani, Wan-Chi Siu, Yuanbo Zhou, Rao Muhammad Umer, Christian Micheloni, Xiaofeng Cong, Rajat Gupta, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Feras Almasri, Thomas Vandamme, Olivier Debeir
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
no code implementations • 3 Sep 2020 • Jingru Tan, Gang Zhang, Hanming Deng, Changbao Wang, Lewei Lu, Quanquan Li, Jifeng Dai
This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020.
Ranked #1 on Instance Segmentation on LVIS v1.0 test-dev
no code implementations • 2 Sep 2020 • Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding
With advancement in deep neural network (DNN), recent state-of-the-art (SOTA) image superresolution (SR) methods have achieved impressive performance using deep residual network with dense skip connections.
1 code implementation • 8 May 2020 • Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte, Michael S. Brown, Yue Cao, Zhilu Zhang, WangMeng Zuo, Xiaoling Zhang, Jiye Liu, Wendong Chen, Changyuan Wen, Meng Liu, Shuailin Lv, Yunchao Zhang, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Xiyu Yu, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Songhyun Yu, Bumjun Park, Jechang Jeong, Shuai Liu, Ziyao Zong, Nan Nan, Chenghua Li, Zengli Yang, Long Bao, Shuangquan Wang, Dongwoon Bai, Jungwon Lee, Youngjung Kim, Kyeongha Rho, Changyeop Shin, Sungho Kim, Pengliang Tang, Yiyun Zhao, Yuqian Zhou, Yuchen Fan, Thomas Huang, Zhihao LI, Nisarg A. Shah, Wei Liu, Qiong Yan, Yuzhi Zhao, Marcin Możejko, Tomasz Latkowski, Lukasz Treszczotko, Michał Szafraniuk, Krzysztof Trojanowski, Yanhong Wu, Pablo Navarrete Michelini, Fengshuo Hu, Yunhua Lu, Sujin Kim, Wonjin Kim, Jaayeon Lee, Jang-Hwan Choi, Magauiya Zhussip, Azamat Khassenov, Jong Hyun Kim, Hwechul Cho, Priya Kansal, Sabari Nathan, Zhangyu Ye, Xiwen Lu, Yaqi Wu, Jiangxin Yang, Yanlong Cao, Siliang Tang, Yanpeng Cao, Matteo Maggioni, Ioannis Marras, Thomas Tanay, Gregory Slabaugh, Youliang Yan, Myungjoo Kang, Han-Soo Choi, Kyungmin Song, Shusong Xu, Xiaomu Lu, Tingniao Wang, Chunxia Lei, Bin Liu, Rajat Gupta, Vineet Kumar
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
1 code implementation • ECCV 2018 • Gang Zhang, Meina Kan, Shiguang Shan, Xilin Chen
The generator contains an attribute manipulation network (AMN) to edit the face image, and a spatial attention network (SAN) to localize the attribute-specific region which restricts the alternation of AMN within this region.