no code implementations • ECCV 2020 • Bin He, Ce Wang, Boxin Shi, Ling-Yu Duan
Frequency aliasing in the digital capture of display screens leads to the moir´e pattern, appearing as stripe-shaped distortions in images.
no code implementations • ECCV 2020 • Guan-Ying Chen, Michael Waechter, Boxin Shi, Kwan-Yee K. Wong, Yasuyuki Matsushita
Based on this insight, we propose a guided calibration network, named GCNet, that explicitly leverages object shape and shading information for improved lighting estimation.
no code implementations • ECCV 2020 • Shuchen Weng, Wenbo Li, Dawei Li, Hongxia Jin, Boxin Shi
We study conditional image repainting where a model is trained to generate visual content conditioned on user inputs, and composite the generated content seamlessly onto a user provided image while preserving the semantics of users' inputs.
no code implementations • 23 Jan 2022 • Tiejun Huang, Yajing Zheng, Zhaofei Yu, Rui Chen, Yuan Li, Ruiqin Xiong, Lei Ma, Junwei Zhao, Siwei Dong, Lin Zhu, Jianing Li, Shanshan Jia, Yihua Fu, Boxin Shi, Si Wu, Yonghong Tian
By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.
no code implementations • NeurIPS 2021 • Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, Boxin Shi
Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms.
no code implementations • 8 Oct 2021 • Liwen Hu, Rui Zhao, Ziluo Ding, Lei Ma, Boxin Shi, Ruiqin Xiong, Tiejun Huang
Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes.
no code implementations • 23 Jul 2021 • Shasha Mao, GuangHui Shi, Licheng Jiao, Shuiping Gou, Yangyang Li, Lin Xiong, Boxin Shi
Based on this, we propose a new method that amends the label distribution of each facial image by leveraging correlations among expressions in the semantic space.
1 code implementation • CVPR 2021 • Qian Zheng, Boxin Shi, Jinnan Chen, Xudong Jiang, Ling-Yu Duan, Alex C. Kot
In this paper, we consider the absorption effect for the problem of single image reflection removal.
no code implementations • CVPR 2021 • Yuchen Hong, Qian Zheng, Lingran Zhao, Xudong Jiang, Alex C. Kot, Boxin Shi
This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes.
no code implementations • CVPR 2021 • Peiqi Duan, Zihao W. Wang, Xinyu Zhou, Yi Ma, Boxin Shi
EventZoom is trained in a noise-to-noise fashion where the two ends of the network are unfiltered noisy events, enforcing noise-free event restoration.
1 code implementation • CVPR 2021 • Heng Guo, Fumio Okura, Boxin Shi, Takuya Funatomi, Yasuhiro Mukaigawa, Yasuyuki Matsushita
To make the problem well-posed, existing MPS methods rely on restrictive assumptions, such as shape prior, surfaces having a monochromatic with uniform albedo.
1 code implementation • CVPR 2021 • Xu Cao, Boxin Shi, Fumio Okura, Yasuyuki Matsushita
Experimental results on analytically computed, synthetic, and real-world surfaces show that our method yields accurate and stable reconstruction for both orthographic and perspective normal maps.
no code implementations • CVPR 2021 • Yajing Zheng, Lingxiao Zheng, Zhaofei Yu, Boxin Shi, Yonghong Tian, Tiejun Huang
Mimicking the sampling mechanism of the fovea, a retina-inspired camera, named spiking camera, is developed to record the external information with a sampling rate of 40, 000 Hz, and outputs asynchronous binary spike streams.
no code implementations • 28 Apr 2021 • Yongjie Zhu, Jiajun Tang, Si Li, Boxin Shi
We propose DeRenderNet, a deep neural network to decompose the albedo and latent lighting, and render shape-(in)dependent shadings, given a single image of an outdoor urban scene, trained in a self-supervised manner.
no code implementations • CVPR 2021 • Yongjie Zhu, yinda zhang, Si Li, Boxin Shi
We train a deep neural network to regress intrinsic cues with physically-based constraints and use them to conduct global and local lightings estimation.
no code implementations • ICCV 2021 • Jin Han, Yixin Yang, Chu Zhou, Chao Xu, Boxin Shi
To reconstruct high-resolution intensity images from event data, we propose EvIntSR-Net that converts event data to multiple latent intensity frames to achieve super-resolution on intensity images in this paper.
no code implementations • NeurIPS 2020 • Zhuokun Yao, Kun Li, Ying Fu, Haofeng Hu, Boxin Shi
For all-pixel operation, we propose the Normal Regression Network to make efficient use of the intra-image spatial information for predicting a surface normal map with rich details.
no code implementations • NeurIPS 2020 • Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi
A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR).
1 code implementation • NeurIPS 2020 • Xu Liu, Chengtao Li, Jian Wang, Jingbo Wang, Boxin Shi, Xiaodong He
In this work, we extended the contextual encoding layer that was originally designed for 2D tasks to 3D Point Cloud scenarios.
10 code implementations • 12 Nov 2020 • Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, Shuchang Zhou
We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video Frame Interpolation (VFI).
Ranked #3 on
Video Frame Interpolation
on Vimeo90K
1 code implementation • 26 Jul 2020 • Guan-Ying Chen, Kai Han, Boxin Shi, Yasuyuki Matsushita, Kwan-Yee K. Wong
To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images.
no code implementations • CVPR 2020 • Yehui Tang, Yunhe Wang, Yixing Xu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu
A graph convolutional neural network is introduced to predict the performance of architectures based on the learned representations and their relation modeled by the graph.
no code implementations • 7 Mar 2020 • Hanting Chen, Yunhe Wang, Han Shu, Changyuan Wen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu
To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators.
2 code implementations • 23 Feb 2020 • Yehui Tang, Yunhe Wang, Yixing Xu, Boxin Shi, Chao Xu, Chunjing Xu, Chang Xu
On one hand, massive trainable parameters significantly enhance the performance of these deep networks.
1 code implementation • CVPR 2020 • Tianyu Guo, Chang Xu, Jiajun Huang, Yunhe Wang, Boxin Shi, Chao Xu, DaCheng Tao
In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality.
no code implementations • IEEE Access ( Volume: 8 ) 2020 • Yanbo Fan, Shuchen Weng, Yong Zhang, Boxin Shi, Yi Zhang
To facilitate end-to-end training, we further develop a scenario context information extraction branch to extract context information from raw RGB video directly.
Ranked #57 on
Skeleton Based Action Recognition
on NTU RGB+D
no code implementations • 18 Jan 2020 • Min Li, Zhenglong Zhou, Zhe Wu, Boxin Shi, Changyu Diao, Ping Tan
From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera.
2 code implementations • CVPR 2020 • Hanting Chen, Yunhe Wang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu
The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.
no code implementations • NeurIPS 2019 • Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, DaCheng Tao
A worst-case formulation can be developed over this distribution set, and then be interpreted as a generation task in an adversarial manner.
1 code implementation • NeurIPS 2019 • Youwei Lyu, Zhaopeng Cui, Si Li, Marc Pollefeys, Boxin Shi
When we take photos through glass windows or doors, the transmitted background scene is often blended with undesirable reflection.
1 code implementation • CVPR 2020 • Shaohui Liu, yinda zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, Zhaopeng Cui
We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function.
1 code implementation • CVPR 2020 • Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu
Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs.
no code implementations • 24 Jul 2019 • Shaodi You, Erqi Huang, Shuaizhe Liang, Yongrong Zheng, Yunxiang Li, Fan Wang, Sen Lin, Qiu Shen, Xun Cao, Diming Zhang, Yuanjiang Li, Yu Li, Ying Fu, Boxin Shi, Feng Lu, Yinqiang Zheng, Robby T. Tan
This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark.
no code implementations • 13 Jul 2019 • Yehui Tang, Shan You, Chang Xu, Boxin Shi, Chao Xu
Specifically, we exploit the unlabeled data to mimic the classification characteristics of giant networks, so that the original capacity can be preserved nicely.
no code implementations • ICCV 2019 • Qian Zheng, Yiming Jia, Boxin Shi, Xudong Jiang, Ling-Yu Duan, Alex C. Kot
This paper solves the Sparse Photometric stereo through Lighting Interpolation and Normal Estimation using a generative Network (SPLINE-Net).
3 code implementations • ICCV 2019 • Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, Qi Tian
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.
no code implementations • ECCV 2020 • Yunhao Ba, Alex Ross Gilbert, Franklin Wang, Jinfa Yang, Rui Chen, Yiqin Wang, Lei Yan, Boxin Shi, Achuta Kadambi
This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning.
1 code implementation • CVPR 2019 • Guan-Ying Chen, Kai Han, Boxin Shi, Yasuyuki Matsushita, Kwan-Yee K. Wong
This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning.
no code implementations • 3 Mar 2019 • Renjie Wan, Boxin Shi, Haoliang Li, Ling-Yu Duan, Alex C. Kot
Face images captured through the glass are usually contaminated by reflections.
1 code implementation • 26 Feb 2019 • Zihao W. Wang, Weixin Jiang, Kuan He, Boxin Shi, Aggelos Katsaggelos, Oliver Cossairt
Temporal Video Frame Synthesis (TVFS) aims at synthesizing novel frames at timestamps different from existing frames, which has wide applications in video codec, editing and analysis.
no code implementations • 30 Jul 2018 • Tianyu Guo, Chang Xu, Shiyi He, Boxin Shi, Chao Xu, DaCheng Tao
In this way, a portable student network with significantly fewer parameters can achieve a considerable accuracy which is comparable to that of teacher network.
no code implementations • CVPR 2018 • Daniel Teo, Boxin Shi, Yinqiang Zheng, Sai-Kit Yeung
We present a self-calibrating polarising radiometric calibration method.
no code implementations • CVPR 2018 • Bing Li, Chia-Wen Lin, Boxin Shi, Tiejun Huang, Wen Gao, C. -C. Jay Kuo
As compared with traditional video retargeting, stereo video retargeting poses new challenges because stereo video contains the depth information of salient objects and its time dynamics.
no code implementations • CVPR 2018 • Zhipeng Mo, Boxin Shi, Feng Lu, Sai-Kit Yeung, Yasuyuki Matsushita
This paper presents a photometric stereo method that works with unknown natural illuminations without any calibration object.
1 code implementation • CVPR 2018 • Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks.
no code implementations • ICCV 2017 • Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems.
no code implementations • ICCV 2017 • Lixiong Chen, Yinqiang Zheng, Boxin Shi, Art Subpa-Asa, Imari Sato
Recent developments in the field have enabled shape recovery techniques for surfaces of various types, but an effective solution to directly estimating the surface normal in the presence of highly specular reflectance remains elusive.
no code implementations • CVPR 2017 • Zhipeng Mo, Boxin Shi, Sai-Kit Yeung, Yasuyuki Matsushita
Radiometrically calibrating the images from Internet photo collections brings photometric analysis from lab data to big image data in the wild, but conventional calibration methods cannot be directly applied to such image data.
no code implementations • CVPR 2017 • Zhaopeng Cui, Jinwei Gu, Boxin Shi, Ping Tan, Jan Kautz
Multi-view stereo relies on feature correspondences for 3D reconstruction, and thus is fundamentally flawed in dealing with featureless scenes.
1 code implementation • 1 Mar 2017 • Nevrez Imamoglu, Chi Zhang, Wataru Shimoda, Yuming Fang, Boxin Shi
As prior knowledge of objects or object features helps us make relations for similar objects on attentional tasks, pre-trained deep convolutional neural networks (CNNs) can be used to detect salient objects on images regardless of the object class is in the network knowledge or not.
no code implementations • CVPR 2016 • Boxin Shi, Zhe Wu, Zhipeng Mo, Dinglong Duan, Sai-Kit Yeung, Ping Tan
Recent progress on photometric stereo extends the technique to deal with general materials and unknown illumination conditions.
no code implementations • ICCV 2015 • Achuta Kadambi, Vage Taamazyan, Boxin Shi, Ramesh Raskar
We propose a framework to overcome these key challenges, allowing the benefits of polarization to be used to enhance depth maps.
no code implementations • ICCV 2015 • Jian Wang, Yasuyuki Matsushita, Boxin Shi, Aswin C. Sankaranarayanan
This paper studies the effect of small angular variations in illumination directions to photometric stereo.