https://sgvr.kaist.ac.kr/publication/iln/
TopIEEE International Conference on Robotics and Automation (ICRA), 2022
https://sgvr.kaist.ac.kr/publication/bmvc2021-in-n-out/
British Machine Vision Conference (BMVC), 2021
Abstract In computer vision, recovering spatial information by filling in masked regions, e.g., inpainting, has been widely investigated for its usability and wide applicability to other various applications: image inpainting, … Continue reading "In-N-Out: Towards Good Initialization for Inpainting and Outpainting"
https://sgvr.kaist.ac.kr/publication/weakly-supervised-contrastive-learning-in-path-manifold-for-monte-carlo-image-reconstruction/
TopACM Trans. on Graphics (ToG) (proc. of SIGGRAPH), 2021
https://sgvr.kaist.ac.kr/publication/unsupsimflow/
TopEuropean Conference on Computer Vision (ECCV), 2020
https://sgvr.kaist.ac.kr/publication/adaptive-incident-radiance-field-sampling-and-reconstruction-using-deep-reinforcement-learning/
TopACM Trans. on Graphics (ToG), 2020
https://sgvr.kaist.ac.kr/publication/coarse-to-fine-clothing-image-generation-with-progressively-constructed-conditional-gan/
International Conference on Computer Vision Theory and Application (VISAPP), 2019
https://sgvr.kaist.ac.kr/publication/accv2018-age-estimation/
Asian Conference on Computer Vision (ACCV), 2018
Source code: ZIP file
Github page
[pdf] Korea Advanced Institute of Science and Technology (KAIST) Figure 1. Overall network framework of our method. In the bottleneck layer, we apply the adaptive triplet ranking strategy (L_T : … Continue reading "ACCV2018 Age Estimation"
https://sgvr.kaist.ac.kr/publication/regional-attention-based-deep-feature-for-image-retrieval/
British Machine Vision Conference(BMVC), 2018
Source code: ZIP file,
Github page