no code implementations • 5 Mar 2024 • Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren, Haoze Sun, Xueyi Zou, Zhensong Zhang, Youliang Yan, Lei Zhu
Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain, facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images.
no code implementations • 27 Feb 2024 • Jiaqi Lin, Zhihao LI, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue Liu, Yangdi Lu, Xiaofei Wu, Songcen Xu, Youliang Yan, Wenming Yang
Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed.
no code implementations • 24 Jan 2024 • Chuan Guo, Yuxuan Mu, Xinxin Zuo, Peng Dai, Youliang Yan, Juwei Lu, Li Cheng
Building upon this, we present a novel generative model that produces diverse stylization results of a single motion (latent) code.
no code implementations • 20 Dec 2023 • Hongyuan Wang, Lizhi Wang, Jiang Xu, Chang Chen, Xue Hu, Fenglong Song, Youliang Yan
By integrating unified spatial-spectral attention and linear dependence, our ECT can model exhaustive correlation within HSI.
no code implementations • 20 Dec 2023 • Xinyuan Liu, Lizhi Wang, Lingen Li, Chang Chen, Xue Hu, Fenglong Song, Youliang Yan
Computational spectral imaging is drawing increasing attention owing to the snapshot advantage, and amplitude, phase, and wavelength encoding systems are three types of representative implementations.
no code implementations • 1 Dec 2023 • Kerui Gu, Zhihao LI, Shiyong Liu, Jianzhuang Liu, Songcen Xu, Youliang Yan, Michael Bi Mi, Kenji Kawaguchi, Angela Yao
Estimating 3D rotations is a common procedure for 3D computer vision.
1 code implementation • 27 Nov 2023 • Haoze Sun, Wenbo Li, Jianzhuang Liu, Haoyu Chen, Renjing Pei, Xueyi Zou, Youliang Yan, Yujiu Yang
We achieve this by marrying image appearance and language understanding to generate a cognitive embedding, which not only activates prior information from large text-to-image diffusion models but also facilitates the generation of high-quality reference images to optimize the SR process.
no code implementations • 5 May 2023 • Jiaming Guo, Xueyi Zou, Yuyi Chen, Yi Liu, Jia Hao, Jianzhuang Liu, Youliang Yan
In recent years, videos and images in 720p (HD), 1080p (FHD) and 4K (UHD) resolution have become more popular for display devices such as TVs, mobile phones and VR.
no code implementations • CVPR 2023 • Renjing Pei, Jianzhuang Liu, Weimian Li, Bin Shao, Songcen Xu, Peng Dai, Juwei Lu, Youliang Yan
Pre-training a vison-language model and then fine-tuning it on downstream tasks have become a popular paradigm.
no code implementations • ICCV 2023 • Peiyan Guan, Renjing Pei, Bin Shao, Jianzhuang Liu, Weimian Li, Jiaxi Gu, Hang Xu, Songcen Xu, Youliang Yan, Edmund Y. Lam
The parallel isomeric attention module is used as the video encoder, which consists of two parallel branches modeling the spatial-temporal information of videos from both patch and frame levels.
Ranked #3 on Video Retrieval on MSR-VTT-1kA
no code implementations • CVPR 2023 • Wei Huang, Chang Chen, Yong Li, Jiacheng Li, Cheng Li, Fenglong Song, Youliang Yan, Zhiwei Xiong
In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation.
no code implementations • CVPR 2023 • Ruikang Xu, Chang Chen, Jingyang Peng, Cheng Li, Yibin Huang, Fenglong Song, Youliang Yan, Zhiwei Xiong
In many computer vision applications (e. g., robotics and autonomous driving), high dynamic range (HDR) data is necessary for object detection algorithms to handle a variety of lighting conditions, such as strong glare.
no code implementations • CVPR 2023 • Jiacheng Li, Chang Chen, Wei Huang, Zhiqiang Lang, Fenglong Song, Youliang Yan, Zhiwei Xiong
Image resampling is a basic technique that is widely employed in daily applications.
no code implementations • ICCV 2023 • Bin Shao, Jianzhuang Liu, Renjing Pei, Songcen Xu, Peng Dai, Juwei Lu, Weimian Li, Youliang Yan
However, compared to image-language pre-training, VLP has lagged far behind due to the lack of large amounts of video-text pairs.
no code implementations • 15 Oct 2022 • Hengsheng Zhang, Xueyi Zou, Jiaming Guo, Youliang Yan, Rong Xie, Li Song
In this paper, considering the characteristics of compressed videos, we propose a Codec Information Assisted Framework (CIAF) to boost and accelerate recurrent VSR models for compressed videos.
3 code implementations • 23 Aug 2022 • Ren Yang, Radu Timofte, Qi Zhang, Lin Zhang, Fanglong Liu, Dongliang He, Fu Li, He Zheng, Weihang Yuan, Pavel Ostyakov, Dmitry Vyal, Magauiya Zhussip, Xueyi Zou, Youliang Yan, Lei LI, Jingzhu Tang, Ming Chen, Shijie Zhao, Yu Zhu, Xiaoran Qin, Chenghua Li, Cong Leng, Jian Cheng, Claudio Rota, Marco Buzzelli, Simone Bianco, Raimondo Schettini, Dafeng Zhang, Feiyu Huang, Shizhuo Liu, Xiaobing Wang, Zhezhu Jin, Bingchen Li, Xin Li, Mingxi Li, Ding Liu, Wenbin Zou, Peijie Dong, Tian Ye, Yunchen Zhang, Ming Tan, Xin Niu, Mustafa Ayazoglu, Marcos Conde, Ui-Jin Choi, Zhuang Jia, Tianyu Xu, Yijian Zhang, Mao Ye, Dengyan Luo, Xiaofeng Pan, Liuhan Peng
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
6 code implementations • 1 Aug 2022 • Zhihao LI, Jianzhuang Liu, Zhensong Zhang, Songcen Xu, Youliang Yan
Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem.
Ranked #1 on Unsupervised 3D Human Pose Estimation on Human3.6M (PA-MPJPE metric)
1 code implementation • 20 May 2022 • Jing Lin, Xiaowan Hu, Yuanhao Cai, Haoqian Wang, Youliang Yan, Xueyi Zou, Yulun Zhang, Luc van Gool
On the other hand, we equip the sequence-to-sequence model with an unsupervised optical flow estimator to maximize its potential.
Ranked #2 on Video Enhancement on MFQE v2
2 code implementations • 20 Apr 2022 • Ren Yang, Radu Timofte, Meisong Zheng, Qunliang Xing, Minglang Qiao, Mai Xu, Lai Jiang, Huaida Liu, Ying Chen, Youcheng Ben, Xiao Zhou, Chen Fu, Pei Cheng, Gang Yu, Junyi Li, Renlong Wu, Zhilu Zhang, Wei Shang, Zhengyao Lv, Yunjin Chen, Mingcai Zhou, Dongwei Ren, Kai Zhang, WangMeng Zuo, Pavel Ostyakov, Vyal Dmitry, Shakarim Soltanayev, Chervontsev Sergey, Zhussip Magauiya, Xueyi Zou, Youliang Yan, Pablo Navarrete Michelini, Yunhua Lu, Diankai Zhang, Shaoli Liu, Si Gao, Biao Wu, Chengjian Zheng, Xiaofeng Zhang, Kaidi Lu, Ning Wang, Thuong Nguyen Canh, Thong Bach, Qing Wang, Xiaopeng Sun, Haoyu Ma, Shijie Zhao, Junlin Li, Liangbin Xie, Shuwei Shi, Yujiu Yang, Xintao Wang, Jinjin Gu, Chao Dong, Xiaodi Shi, Chunmei Nian, Dong Jiang, Jucai Lin, Zhihuai Xie, Mao Ye, Dengyan Luo, Liuhan Peng, Shengjie Chen, Qian Wang, Xin Liu, Boyang Liang, Hang Dong, Yuhao Huang, Kai Chen, Xingbei Guo, Yujing Sun, Huilei Wu, Pengxu Wei, Yulin Huang, Junying Chen, Ik Hyun Lee, Sunder Ali Khowaja, Jiseok Yoon
This challenge includes three tracks.
1 code implementation • 6 Jan 2022 • Jing Lin, Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Youliang Yan, Xueyi Zou, Henghui Ding, Yulun Zhang, Radu Timofte, Luc van Gool
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring.
Ranked #1 on Deblurring on DVD
no code implementations • 17 Dec 2021 • Lin Liu, Shanxin Yuan, Jianzhuang Liu, Xin Guo, Youliang Yan, Qi Tian
For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders.
no code implementations • ICCV 2021 • Tao Wang, Yong Li, Jingyang Peng, Yipeng Ma, Xian Wang, Fenglong Song, Youliang Yan
One is a 1D weight vector used for image-level scenario adaptation, the other is a 3D weight map aimed for pixel-wise category fusion.
1 code implementation • 3 Aug 2021 • Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan
The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.
no code implementations • 7 Jun 2021 • Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, Xueyi Zou
This paper reviews the NTIRE2021 challenge on burst super-resolution.
no code implementations • 17 May 2021 • Andrey Ignatov, Andres Romero, Heewon Kim, Radu Timofte, Chiu Man Ho, Zibo Meng, Kyoung Mu Lee, Yuxiang Chen, Yutong Wang, Zeyu Long, Chenhao Wang, Yifei Chen, Boshen Xu, Shuhang Gu, Lixin Duan, Wen Li, Wang Bofei, Zhang Diankai, Zheng Chengjian, Liu Shaoli, Gao Si, Zhang Xiaofeng, Lu Kaidi, Xu Tianyu, Zheng Hui, Xinbo Gao, Xiumei Wang, Jiaming Guo, Xueyi Zhou, Hao Jia, Youliang Yan
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
1 code implementation • 17 May 2021 • Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Andrew Lek, Mustafa Ayazoglu, Jie Liu, Zongcai Du, Jiaming Guo, Xueyi Zhou, Hao Jia, Youliang Yan, Zexin Zhang, Yixin Chen, Yunbo Peng, Yue Lin, Xindong Zhang, Hui Zeng, Kun Zeng, Peirong Li, Zhihuang Liu, Shiqi Xue, Shengpeng Wang
Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices.
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+.
7 code implementations • CVPR 2020 • Rufeng Zhang, Zhi Tian, Chunhua Shen, Mingyu You, Youliang Yan
To date, instance segmentation is dominated by twostage methods, as pioneered by Mask R-CNN.
9 code implementations • CVPR 2020 • Hao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen, Yongming Huang, Youliang Yan
The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference.
Ranked #13 on Real-time Instance Segmentation on MSCOCO
2 code implementations • ICCV 2019 • Haokui Zhang, Chunhua Shen, Ying Li, Yuanzhouhan Cao, Yu Liu, Youliang Yan
The temporal consistency loss is combined with the spatial loss to update the model in an end-to-end fashion.
Ranked #5 on Monocular Depth Estimation on Mid-Air Dataset
3 code implementations • ICCV 2019 • Wei Yin, Yifan Liu, Chunhua Shen, Youliang Yan
Monocular depth prediction plays a crucial role in understanding 3D scene geometry.
Ranked #10 on Depth Estimation on NYU-Depth V2
1 code implementation • CVPR 2019 • Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan
To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride.
no code implementations • CVPR 2019 • Zhi Tian, Tong He, Chunhua Shen, Youliang Yan
In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs.
Ranked #46 on Semantic Segmentation on PASCAL Context