Our paper has been accepted to ECCV
Facial Depth and Normal Estimation using Single Dual-Pixel CameraMinjun Kang, Jaesung Choe, Hyowon Ha, Hae-Gon Jeon, Sunghoon Im, In So Kweon and Kuk-Jin YoonEuropean Conference on Computer Vision (ECCV), Oct …
Facial Depth and Normal Estimation using Single Dual-Pixel CameraMinjun Kang, Jaesung Choe, Hyowon Ha, Hae-Gon Jeon, Sunghoon Im, In So Kweon and Kuk-Jin YoonEuropean Conference on Computer Vision (ECCV), Oct …
RVMOS: Range-View Moving Object Segmentation Leveraged by Semantic and Motion FeaturesJaeyeul Kim*, Jungwan Woo*, Sunghoon ImIEEE Robotics and Automation Letters (RAL), Accepted– This paper will be presented at IEEE/RSJ International …
Self-supervised Monocular Depth and Motion Learning in Dynamic Scenes: Semantic Prior to RescueSeokju Lee, Francois Rameau, Sunghoon Im, In So KweonInternational Journal of Computer Vision (IJCV), Accepted
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic SegmentationSeunghun Lee, Wonhyeok Choi, Changjae Kim, Minwoo Choi, Sunghoon ImIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Article …
CMSNet: Deep Color and Monochrome StereoHae-Gon Jeon, Sunghoon Im, Jaesung Choe, Minjun Kang, Joon-Young Lee, Martial HebertInternational Journal of Computer Vision (IJCV), Accepted
ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip AttackDahoon Park, Kon-Woo Kwon, Sunghoon Im, Jaeha KungBritish Machine Vision Conference (BMVC), 2021
VolumeFusion: Deep Depth Fusion for 3D Scene ReconstructionJaesung Choe, Sunghoon Im, François Rameau, Minjun Kang, In So KweonIEEE International Conference on Computer Vision (ICCV), 2021
A Large-scale Virtual Dataset and Egocentric Localization for Disaster Responses Hae-Gon Jeon, Sunghoon Im, Byeong-Uk Lee, François Rameau, Dong-Geol Choi, Jean Oh, In So Kweon, and Martial Hebert IEEE Transactions …
ProFeat: Unsupervised Image Clustering via Progressive Feature RefinementJeonghoon Kim, Sunghoon Im, Sunghyun ChoCVPR workshop on Learning From Limited or Imperfect Data (CVPRw), 2021
DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain AdaptationSeunghun Lee, Sunghyun Cho, Sunghoon ImIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Article : http://www.aitimes.com/news/articleView.html?idxno=139612