no code implementations • 16 Apr 2024 • Wenjie Lin, Zhen Liu, Chengzhi Jiang, Mingyan Han, Ting Jiang, Shuaicheng Liu
In this paper, we address the Bracket Image Restoration and Enhancement (BracketIRE) task using a novel framework, which requires restoring a high-quality high dynamic range (HDR) image from a sequence of noisy, blurred, and low dynamic range (LDR) multi-exposure RAW inputs.
1 code implementation • 28 Mar 2024 • Tianhao Zhou, Haipeng Li, Ziyi Wang, Ao Luo, Chen-Lin Zhang, Jiajun Li, Bing Zeng, Shuaicheng Liu
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing.
1 code implementation • 27 Mar 2024 • Hao Xu, Haipeng Li, Yinqiao Wang, Shuaicheng Liu, Chi-Wing Fu
Reconstructing 3D hand mesh robustly from a single image is very challenging, due to the lack of diversity in existing real-world datasets.
no code implementations • 12 Mar 2024 • Yi Zeng, Zhengning Wang, Yuxuan Liu, Tianjiao Zeng, Xuhang Liu, Xinglong Luo, Shuaicheng Liu, Shuyuan Zhu, Bing Zeng
Since texture details intertwine with compression artifacts in compressed dark images, detail enhancement and blocking artifacts suppression contradict each other in image space.
1 code implementation • 11 Mar 2024 • Lang Nie, Chunyu Lin, Kang Liao, Yun Zhang, Shuaicheng Liu, Yao Zhao
In this paper, we retarget video stitching to an emerging issue, named warping shake, when extending image stitching to video stitching.
1 code implementation • 24 Jan 2024 • Lang Nie, Chunyu Lin, Kang Liao, Shuaicheng Liu, Yao Zhao
To break this bottleneck, we propose the coupled thin-plate spline model (CoupledTPS), which iteratively couples multiple TPS with limited control points into a more flexible and powerful transformation.
1 code implementation • 14 Dec 2023 • Ru Li, Jia Liu, Guanghui Liu, Shengping Zhang, Bing Zeng, Shuaicheng Liu
We modify the classical spectral rendering into two main steps, 1) the generation of a series of spectrum maps spanning different wavelengths, 2) the combination of these spectrum maps for the RGB output.
1 code implementation • ICCV 2023 • Ao Luo, Fan Yang, Xin Li, Lang Nie, Chunyu Lin, Haoqiang Fan, Shuaicheng Liu
Moreover, for reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM) which not only inherits properties from Gaussian distribution to instinctively revolve around the neighbor fields of each point but also is empowered to put the emphasis on contextually related regions during matching.
1 code implementation • ICCV 2023 • Ting Jiang, Chuan Wang, Xinpeng Li, Ru Li, Haoqiang Fan, Shuaicheng Liu
In this paper, we introduce a new approach for high-quality multi-exposure image fusion (MEF).
1 code implementation • ICCV 2023 • Guangyang Wu, Xiaohong Liu, Kunming Luo, Xi Liu, Qingqing Zheng, Shuaicheng Liu, Xinyang Jiang, Guangtao Zhai, Wenyi Wang
To train and evaluate the proposed AccFlow, we have constructed a large-scale high-quality dataset named CVO, which provides ground-truth optical flow labels between adjacent and distant frames.
no code implementations • ICCV 2023 • Yinglong Wang, Zhen Liu, Jianzhuang Liu, Songcen Xu, Shuaicheng Liu
We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks, which enables the correction factor gamma to be learned in a coarse to elaborate manner via adaptively perceiving the deviated illumination.
1 code implementation • ICCV 2023 • Hai Jiang, Haipeng Li, Songchen Han, Haoqiang Fan, Bing Zeng, Shuaicheng Liu
In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data and yield a supervised homography network.
no code implementations • ICCV 2023 • Junpeng Jing, Jiankun Li, Pengfei Xiong, Jiangyu Liu, Shuaicheng Liu, Yichen Guo, Xin Deng, Mai Xu, Lai Jiang, Leonid Sigal
A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios.
1 code implementation • 10 Jul 2023 • Xinpeng Li, Ting Jiang, Haoqiang Fan, Shuaicheng Liu
Our experiments confirm the powerful feature extraction capabilities of Segment Anything and highlight the value of combining spatial-domain and frequency-domain features in IQA tasks.
no code implementations • 9 Jun 2023 • Haipeng Li, Dingrui Liu, Yu Zeng, Shuaicheng Liu, Tao Gan, Nini Rao, Jinlin Yang, Bing Zeng
On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data.
1 code implementation • 1 Jun 2023 • Hai Jiang, Ao Luo, Songchen Han, Haoqiang Fan, Shuaicheng Liu
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration.
Ranked #1 on Low-Light Image Enhancement on LOLv2
no code implementations • 23 May 2023 • Qi Wu, Mingyan Han, Ting Jiang, Haoqiang Fan, Bing Zeng, Shuaicheng Liu
Deep image denoising models often rely on large amount of training data for the high quality performance.
no code implementations • 14 Apr 2023 • Lei Yu, Xinpeng Li, Youwei Li, Ting Jiang, Qi Wu, Haoqiang Fan, Shuaicheng Liu
To address this issue, we propose a novel multi-stage lightweight network boosting method, which can enable lightweight networks to achieve outstanding performance.
no code implementations • 10 Apr 2023 • Ru Li, Guanghui Liu, Bing Zeng, Shuaicheng Liu
The method combines the efficiency of optical flow and the accuracy of PatchMatch.
no code implementations • ICCV 2023 • Xinglong Luo, Kunming Luo, Ao Luo, Zhengning Wang, Ping Tan, Shuaicheng Liu
Previous datasets are created by either capturing real scenes by event cameras or synthesizing from images with pasted foreground objects.
1 code implementation • ICCV 2023 • Lang Nie, Chunyu Lin, Kang Liao, Shuaicheng Liu, Yao Zhao
First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion.
no code implementations • 23 Jan 2023 • Haipeng Li, Kunming Luo, Bing Zeng, Shuaicheng Liu
Second, we design a self-guided fusion module (SGF) to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details.
1 code implementation • ICCV 2023 • Changxing Deng, Ao Luo, Haibin Huang, Shaodan Ma, Jiangyu Liu, Shuaicheng Liu
In this paper, we propose a novel framework for optical flow estimation that achieves a good balance between performance and efficiency.
1 code implementation • ICCV 2023 • Suyi Chen, Hao Xu, Ru Li, Guanghui Liu, Chi-Wing Fu, Shuaicheng Liu
We design SIRA-PCR, a new approach to 3D point cloud registration.
1 code implementation • ICCV 2023 • Weilong Yan, Robby T. Tan, Bing Zeng, Shuaicheng Liu
In this work, we adopt a more straightforward method to learn deep homography mixture motion between an RS image and its corresponding GS image, without large solution space or strict restrictions on image features.
no code implementations • 23 Dec 2022 • Yuqiao Liu, Haipeng Li, Yanan sun, Shuaicheng Liu
NAS without training (WOT) score is such a metric, which estimates the final trained accuracy of the architecture through the ability to distinguish different inputs in the activation layer.
1 code implementation • CVPR 2022 • Heng Li, Zhaopeng Cui, Shuaicheng Liu, Ping Tan
Our graph optimizer iteratively refines the global camera rotations by minimizing each node's single rotation objective function.
1 code implementation • 6 Dec 2022 • Hai Jiang, Haipeng Li, Yuhang Lu, Songchen Han, Shuaicheng Liu
Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field.
1 code implementation • ICCV 2023 • Zhuofan Zhang, Zhen Liu, Ping Tan, Bing Zeng, Shuaicheng Liu
In this work, we adopt recent off-the-shelf high-quality deep motion models for motion estimation to recover the camera trajectory and focus on the latter two steps.
2 code implementations • 7 Nov 2022 • Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
2 code implementations • 24 Aug 2022 • Ziwei Luo, Youwei Li, Lei Yu, Qi Wu, Zhihong Wen, Haoqiang Fan, Shuaicheng Liu
The proposed nearest convolution has the same performance as the nearest upsampling but is much faster and more suitable for Android NNAPI.
3 code implementations • 10 Aug 2022 • Zhen Liu, Yinglong Wang, Bing Zeng, Shuaicheng Liu
High dynamic range (HDR) deghosting algorithms aim to generate ghost-free HDR images with realistic details.
1 code implementation • 22 Jul 2022 • Yunhui Han, Kunming Luo, Ao Luo, Jiangyu Liu, Haoqiang Fan, Guiming Luo, Shuaicheng Liu
Specifically, we first estimate optical flow between a pair of video frames, and then synthesize a new image from this pair based on the predicted flow.
1 code implementation • 7 Jul 2022 • Lang Nie, Chunyu Lin, Kang Liao, Shuaicheng Liu, Yao Zhao
To this end, we leverage a neural network to predict the optical flows that can warp the tilted images to be perceptually horizontal.
no code implementations • 25 May 2022 • Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
no code implementations • 24 May 2022 • Yifeng Zhou, Xing Xu, Shuaicheng Liu, Guoqing Wang, Huimin Lu, Heng Tao Shen
To achieve promising results on removing noise from real-world images, most of existing denoising networks are formulated with complex network structure, making them impractical for deployment.
2 code implementations • 11 May 2022 • Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
1 code implementation • CVPR 2022 • Mingbo Hong, Yuhang Lu, Nianjin Ye, Chunyu Lin, Qijun Zhao, Shuaicheng Liu
Estimating homography from an image pair is a fundamental problem in image alignment.
1 code implementation • 18 Apr 2022 • Ziwei Luo, Youwei Li, Shen Cheng, Lei Yu, Qi Wu, Zhihong Wen, Haoqiang Fan, Jian Sun, Shuaicheng Liu
To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantly improve the capability of extracting inter-frame information and reconstruction.
Ranked #1 on Burst Image Super-Resolution on SyntheticBurst
3 code implementations • CVPR 2022 • Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, Shuaicheng Liu
With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress.
1 code implementation • CVPR 2022 • Lang Nie, Chunyu Lin, Kang Liao, Shuaicheng Liu, Yao Zhao
In this paper, we address these issues by proposing the first deep learning solution to image rectangling.
2 code implementations • CVPR 2022 • Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, Shuaicheng Liu
In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules.
Ranked #1 on Blind Super-Resolution on DIV2KRK - 2x upscaling
1 code implementation • 8 Feb 2022 • Ao Luo, Fan Yang, Kunming Luo, Xin Li, Haoqiang Fan, Shuaicheng Liu
Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph.
no code implementations • CVPR 2022 • Luwei Yang, Rakesh Shrestha, Wenbo Li, Shuaicheng Liu, Guofeng Zhang, Zhaopeng Cui, Ping Tan
Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image.
1 code implementation • CVPR 2022 • Ao Luo, Fan Yang, Xin Li, Shuaicheng Liu
Optical flow is a fundamental method used for quantitative motion estimation on the image plane.
no code implementations • CVPR 2022 • Chengzhou Tang, Yuqiang Yang, Bing Zeng, Ping Tan, Shuaicheng Liu
To these ends, we design a method that receives a low-resolution RAW as the input and estimates the desired higher-resolution RAW jointly with the degradation model.
1 code implementation • CVPR 2022 • Fushun Zhu, Shan Zhao, Peng Wang, Hao Wang, Hua Yan, Shuaicheng Liu
We propose a semi-supervised network for wide-angle portraits correction.
1 code implementation • ICCV 2021 • Cheng Zhang, Zhaopeng Cui, Cai Chen, Shuaicheng Liu, Bing Zeng, Hujun Bao, yinda zhang
Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods.
1 code implementation • 6 Jul 2021 • Lang Nie, Chunyu Lin, Kang Liao, Shuaicheng Liu, Yao Zhao
Homography estimation is an important task in computer vision applications, such as image stitching, video stabilization, and camera calibration.
1 code implementation • 24 Jun 2021 • Lang Nie, Chunyu Lin, Kang Liao, Shuaicheng Liu, Yao Zhao
Even compared with the supervised solutions, our image stitching quality is still preferred by users.
2 code implementations • Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2021 • Ziwei Luo, Lei Yu, Xuan Mo, Youwei Li, Lanpeng Jia, Haoqiang Fan, Jian Sun, Shuaicheng Liu
We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR).
Ranked #2 on Burst Image Super-Resolution on SyntheticBurst
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.
3 code implementations • 7 Jun 2021 • Hao Xu, Nianjin Ye, Guanghui Liu, Bing Zeng, Shuaicheng Liu
Data association is important in the point cloud registration.
8 code implementations • 22 May 2021 • Zhen Liu, Wenjie Lin, Xinpeng Li, Qing Rao, Ting Jiang, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet.
Ranked #5 on Face Alignment on WFW (Extra Data)
no code implementations • 17 May 2021 • Andrey Ignatov, Kim Byeoung-su, Radu Timofte, Angeline Pouget, Fenglong Song, Cheng Li, Shuai Xiao, Zhongqian Fu, Matteo Maggioni, Yibin Huang, Shen Cheng, Xin Lu, Yifeng Zhou, Liangyu Chen, Donghao Liu, Xiangyu Zhang, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Minsu Kwon, Myungje Lee, Jaeyoon Yoo, Changbeom Kang, Shinjo Wang, Bin Huang, Tianbao Zhou, Shuai Liu, Lei Lei, Chaoyu Feng, Liguang Huang, Zhikun Lei, Feifei Chen
A detailed description of all models developed in the challenge is provided in this paper.
1 code implementation • CVPR 2021 • Jing Tan, Shan Zhao, Pengfei Xiong, Jiangyu Liu, Haoqiang Fan, Shuaicheng Liu
Wide-angle portraits often enjoy expanded views.
no code implementations • 8 Apr 2021 • Kunming Luo, Ao Luo, Chuan Wang, Haoqiang Fan, Shuaicheng Liu
Equipped with these two modules, our method achieves the best performance for unsupervised optical flow estimation on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
1 code implementation • ICCV 2021 • Nianjin Ye, Chuan Wang, Haoqiang Fan, Shuaicheng Liu
Last, we propose a Feature Identity Loss (FIL) to enforce the learned image feature warp-equivariant, meaning that the result should be identical if the order of warp operation and feature extraction is swapped.
1 code implementation • 26 Mar 2021 • Youwei Li, Haibin Huang, Lanpeng Jia, Haoqiang Fan, Shuaicheng Liu
Rethinking both, we learn the distribution of underlying high-frequency details in a discrete form and propose a two-stage pipeline: divergence stage to convergence stage.
2 code implementations • ICCV 2021 • Haipeng Li, Kunming Luo, Shuaicheng Liu
Experiments show that our method outperforms the state-of-art methods in both regular and challenging scenes.
1 code implementation • CVPR 2021 • Cheng Zhang, Zhaopeng Cui, yinda zhang, Bing Zeng, Marc Pollefeys, Shuaicheng Liu
We not only propose an image-based local structured implicit network to improve the object shape estimation, but also refine the 3D object pose and scene layout via a novel implicit scene graph neural network that exploits the implicit local object features.
Ranked #1 on Monocular 3D Object Detection on SUN RGB-D (using extra training data)
1 code implementation • ICCV 2021 • Hao Xu, Shuaicheng Liu, Guangfu Wang, Guanghui Liu, Bing Zeng
On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features.
no code implementations • 3 Feb 2021 • Ru Li, Chuan Wang, Jue Wang, Guanghui Liu, Heng-Yu Zhang, Bing Zeng, Shuaicheng Liu
The ground truth images play a leading role in generating reasonable HDR images.
1 code implementation • 27 Jan 2021 • Haipeng Li, Shuaicheng Liu, Jue Wang
In this work, we propose a deep network that compensates the motions caused by the OIS, such that the gyroscopes can be used for image alignment on the OIS cameras.
no code implementations • 19 Jan 2021 • Ru Li, Shuaicheng Liu, Guangfu Wang, Guanghui Liu, Bing Zeng
We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders.
4 code implementations • CVPR 2021 • Shen Cheng, Yuzhi Wang, Haibin Huang, Donghao Liu, Haoqiang Fan, Shuaicheng Liu
Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.
Ranked #1 on Image Denoising on SIDD (SSIM (sRGB) metric)
2 code implementations • CVPR 2021 • Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, Jian Sun
By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Ranked #1 on Optical Flow Estimation on Sintel Final unsupervised
1 code implementation • 15 Sep 2020 • Jing Wu, Mingyi Zhou, Shuaicheng Liu, Yipeng Liu, Ce Zhu
A single perturbation can pose the most natural images to be misclassified by classifiers.
no code implementations • 30 Jun 2020 • Kunming Luo, Chuan Wang, Nianjin Ye, Shuaicheng Liu, Jue Wang
Occlusion is an inevitable and critical problem in unsupervised optical flow learning.
no code implementations • 9 Jun 2020 • Qingdong He, Zhengning Wang, Hao Zeng, Yijun Liu, Shuaicheng Liu, Bing Zeng
After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method.
2 code implementations • CVPR 2020 • Mingyi Zhou, Jing Wu, Yipeng Liu, Shuaicheng Liu, Ce Zhu
In this paper, we propose a data-free substitute training method (DaST) to obtain substitute models for adversarial black-box attacks without the requirement of any real data.
no code implementations • 28 Mar 2020 • Mingyi Zhou, Jing Wu, Yipeng Liu, Xiaolin Huang, Shuaicheng Liu, Xiang Zhang, Ce Zhu
Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model.
no code implementations • 16 Mar 2020 • Jiaxiong Qiu, Cai Chen, Shuaicheng Liu, Bing Zeng
The channel redundancy in feature maps of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources.
no code implementations • 11 Dec 2019 • Nianjin Ye, Chuan Wang, Shuaicheng Liu, Lanpeng Jia, Jue Wang, Yongqing Cui
Deep homography methods, on the other hand, are free from such problem by learning deep features for robust performance.
1 code implementation • CVPR 2020 • Peng Dai, yinda zhang, Zhuwen Li, Shuaicheng Liu, Bing Zeng
The input to the network is the raw point cloud of a scene and the output are image or image sequences from a novel view or along a novel camera trajectory.
1 code implementation • 10 Dec 2019 • Shuaicheng Liu, Zehao Zhang, Kai Song, Bing Zeng
The unprecedented performance achieved by deep convolutional neural networks for image classification is linked primarily to their ability of capturing rich structural features at various layers within networks.
1 code implementation • 2 Nov 2019 • Jiaxiong Qiu, Xinyuan Yu, Guoqiang Yang, Shuaicheng Liu
Outdoor vision robotic systems and autonomous cars suffer from many image-quality issues, particularly haze, defocus blur, and motion blur, which we will define generically as "blindness issues".
1 code implementation • ECCV 2020 • Jirong Zhang, Chuan Wang, Shuaicheng Liu, Lanpeng Jia, Nianjin Ye, Jue Wang, Ji Zhou, Jian Sun
Homography estimation is a basic image alignment method in many applications.
Ranked #5 on Homography Estimation on S-COCO
1 code implementation • CVPR 2019 • Chao Zhang, Shuaicheng Liu, Xun Xu, Ce Zhu
Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models.
Ranked #3 on Age Estimation on FGNET
no code implementations • 20 Dec 2018 • Yinglong Wang, Shuaicheng Liu, Chen Chen, Dehua Xie, Bing Zeng
We present a novel rain removal method in this paper, which consists of two steps, i. e., detection of rain streaks and reconstruction of the rain-removed image.
no code implementations • 19 Dec 2018 • Yinglong Wang, Shuaicheng Liu, Bing Zeng
Removing rain streaks from a single image continues to draw attentions today in outdoor vision systems.
1 code implementation • CVPR 2019 • Jiaxiong Qiu, Zhaopeng Cui, yinda zhang, Xingdi Zhang, Shuaicheng Liu, Bing Zeng, Marc Pollefeys
In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth.
2 code implementations • 22 Nov 2018 • Xiandong Meng, Xuan Deng, Shuyuan Zhu, Shuaicheng Liu, Chuan Wang, Chen Chen, Bing Zeng
In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames.
no code implementations • CVPR 2017 • Kaimo Lin, Nianjuan Jiang, Shuaicheng Liu, Loong-Fah Cheong, Minh Do, Jiangbo Lu
The choice of motion models is vital in applications like image/video stitching and video stabilization.
no code implementations • CVPR 2014 • Shuaicheng Liu, Lu Yuan, Ping Tan, Jian Sun
We propose a novel motion model, SteadyFlow, to represent the motion between neighboring video frames for stabilization.