Robotics and Computer Vision Lab

Publications

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저 자 Tae-Hyun Oh, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon
학 회 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
Notes <iframe width="560" height="315" src="https://www.youtube.com/embed/PzE31ZpG4_I" frameborder="0" allowfullscreen></iframe>
논문일시(Year) 2015
논문일시(Month) 06
http://thohkaistackr.wix.com/page#!projecttpami15/ca3vRank minimization problem can be boiled down to either Nuclear Norm Minimization (NNM) or Weighted NNM (WNNM) problem. The problems related to NNM (or WNNM) can be solved iteratively by applying a closedform proximal operator, called Singular Value Thresholding (SVT) (or Weighted SVT), but they suffer from high computational cost to compute a Singular Value Decomposition (SVD) at each iteration. In this paper, we propose an accurate and fast approximation method for SVT, called fast randomized SVT (FRSVT), where we avoid direct computation of SVD. The key idea is to extract an approximate basis for the range of a matrix from its compressed matrix.
Given the basis, we compute the partial singular values of the original matrix from a small factored matrix. While the basis approximation is the bottleneck, our method is already severalfold faster than thin SVD. By adopting a range propagation technique, we can further avoid one of the bottleneck at each iteration. Our theoretical analysis provides a stepping stone between the approximation bound of SVD
and its effect to NNM via SVT. Along with the analysis, our empirical results on both quantitative and qualitative studies show our approximation rarely harms the convergence behavior of the host algorithms. We apply it and validate the efficiency of our method on various vision problems, e.g. subspace clustering, weather artifact removal, simultaneous multi-image alignment and rectification.


[Project page]
Matlab souce code is available.

[Acknowledgment (NCRC)];
The first author was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028680).

[Award]
* This work was awarded by Samsung HumanTech Paper competition as Gold Prize (1st place).

List of Articles
339. Non-Local Spatial Propagation Network for Depth Completion
Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, In So Kweon
European Conference on Computer Vision (ECCV) 2020 / 08
338. Two-Phase Pseudo Label Densification for Self-training based Domain Adaptation
Inkyu Shin, Sanghyun Woo, Fei Pan, In So Kweon
European Conference on Computer Vision (ECCV) 2020 / 08
337. Global-and-Local Relative Position Embedding for Unsupervised Video Summarization
Yunjae Jung, Donghyeon Cho, Sanghyun Woo, In So Kweon
European Conference on Computer Vision (ECCV) 2020 / 08
336. Detecting Human-Object Interactions with Action Co-occurrence Priors
Dong-Jin Kim, Xiao Sun, Jinsoo Choi, Stephen Lin, and In So Kweon
European Conference on Computer Vision (ECCV) 2020 / 08
335. SideGuide: A Large-scale Sidewalk Dataset for Guiding Impaired People
Kibaek Park*, Youngtaek Oh*, Soomin Ham*, Kyungdon Joo*, HYOKYOUNG KIM, HyoYoung Kum, In So Kweon
IROS, 2020 2020 / 10
334. Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations
Chaoning Zhang*, Philipp Benz*, Tooba Imtiaz, In So Kweon (Chaoning Zhang, Philipp Benz are co-first author)
Computer Vision and Pattern Recognition, CVPR, 2020. 2020 / 06
333. Video Panoptic Segmentation
Dahun Kim, Sanghyun Woo, Joon-Young Lee, In So Kweon
Computer Vision and Pattern Recognition, CVPR, 2020. 2020 / 06
332. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Fei Pan, Inkyu Shin, Francois Rameau, Seokju Lee, In So Kweon
Computer Vision and Pattern Recognition, CVPR, 2020. 2020 / 06
331. Robust Reference-based Super-Resolution with Similarity-Aware Deformable Convolution
Gyumin Shim, Jinsun Park, In So Kweon
Computer Vision and Pattern Recognition, CVPR, 2020. 2020 / 06
330. Salient View Selection for Visual Recognition of Industrial Components
Seong-heum Kim, Gyeongmin Choe, Min-Gyu Park, In So Kweon
IEEE International Conference on Robotics and Automation (ICRA) 2020 / 05
329. Linear RGB-D SLAM for Atlanta World
Kyungdon Joo, Tae-Hyun Oh, Francois Rameau, Jean-Charles Bazin and In So Kweon
IEEE International Conference on Robotics and Automation (ICRA) 2020 / 05
328. Globally Optimal Relative Pose Estimation for Camera on a Selfie Stick
Kyungdon Joo, Hongdong Li, Tae-Hyun Oh, Yunsu Bok and and In So Kweon
IEEE International Conference on Robotics and Automation (ICRA) 2020 / 05
327. CNN-based Simultaneous Dehazing and Depth Estimation
Byeong-Uk Lee, Kyunghyun Lee, Jean Oh and In So Kweon
IEEE International Conference on Robotics and Automation (ICRA) 2020 / 05
326. Depth Completion with Deep Geometry and Context Guidance
Byeong-Uk Lee, Hae-Gon Jeon, Sunghoon Im, In So Kweon
IEEE International Conference on Robotics and Automation (ICRA) 2019 / 05
325. Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling
Yunjae Jung, Dahun Kim, Sanghyun Woo, Kyungsu Kim, Sungjin Kim, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2020 / 02
324. CD-UAP: Class Discriminative Universal Adversarial Perturbations
Chaoning Zhang*, Philipp Benz*, Tooba Imtiaz, In-So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2020 / 02
323. DeepPTZ: Deep Self-Calibration for PTZ Cameras
Chaoning Zhang, Francois Rameau, Junsik Kim, Dawit Mureja Argaw, Jean-Charles Bazin, and In So Kweon
IEEE Winter Conference on Applications of Computer Vision (WACV) 2020 / 03
322. Propose-and-Attend Single Shot Detector
Ho-Deok Jang, Sanghyun Woo, Philipp Benz, Jinsun Park, and In So Kweon
IEEE Winter Conference on Applications of Computer Vision (WACV) 2020 / 03
321. Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach
Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh, In So Kweon
International Conference on Empirical Methods in Natural Language Processing (EMNLP) 2019 / 11
320. Visuomotor Understanding for Representation Learning of Driving Scenes
Seokju Lee, Junsik Kim, Tae-Hyun Oh, Yongseop Jeong, Donggeun Yoo, Stephen Lin, In So Kweon
British Machine Vision Conference (BMVC) 2019 / 9
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