High-dimensional Feature Extraction for Denoising Monte Carlo Renderings몬테카를로 렌더링의 노이즈 제거를 위한 고차원 피처 추출
https://sgvr.kaist.ac.kr/publication/%eb%aa%ac%ed%85%8c%ec%b9%b4%eb%a5%bc%eb%a1%9c-%eb%a0%8c%eb%8d%94%eb%a7%81%ec%9d%98-%eb%85%b8%ec%9d%b4%ec%a6%88-%ec%a0%9c%ea%b1%b0%eb%a5%bc-%ec%9c%84%ed%95%9c-%ea%b3%a0%ec%b0%a8%ec%9b%90-%ed%94%bc/
조인영, Yuchi Huo, and 윤성의
한국컴퓨터그래픽스학회(KCGS), 2020
학습 기반 상태 전이 함수를 사용한 Kinodynamic 플래닝
https://sgvr.kaist.ac.kr/publication/%ed%95%99%ec%8a%b5-%ea%b8%b0%eb%b0%98-%ec%83%81%ed%83%9c-%ec%a0%84%ec%9d%b4-%ed%95%a8%ec%88%98%eb%a5%bc-%ec%82%ac%ec%9a%a9%ed%95%9c-kinodynamic-%ed%94%8c%eb%9e%98%eb%8b%9d/
장진혁 and 윤성의
대한전자공학회(IEIE), 2020
Unsupervised Learning of Optical Flow with Deep Feature Similarity
https://sgvr.kaist.ac.kr/publication/unsupsimflow/
Woobin Im, Tae-Kyun Kim, and Sung-Eui Yoon
TopEuropean Conference on Computer Vision (ECCV), 2020
Adaptive Incident Radiance Field Sampling and Reconstruction Using Deep Reinforcement Learning
https://sgvr.kaist.ac.kr/publication/adaptive-incident-radiance-field-sampling-and-reconstruction-using-deep-reinforcement-learning/
YUCHI HUO, RUI WANG, RUZAHNG ZHENG, HUALIN XU, HUJUN BAO, and SUNG-EUI YOON
TopACM Trans. on Graphics (ToG), 2020
Chosen as the cover image of the journal issue
Paper Sup. report
Two-stream Spatiotemporal Feature for Video QA Task
https://sgvr.kaist.ac.kr/publication/two-stream-spatiotemporal-feature-for-video-qa-task/
Chiwan Song, Woobin Im, and Sung-eui Yoon
Arxiv, 2019
Coarse-to-Fine Clothing Image Generation with Progressively Constructed Conditional GAN
https://sgvr.kaist.ac.kr/publication/coarse-to-fine-clothing-image-generation-with-progressively-constructed-conditional-gan/
Youngki Kwon, Soomin Kim, Donggeun Yoo, and Sung-Eui Yoon
International Conference on Computer Vision Theory and Application (VISAPP), 2019
Scale-Varying Triplet Ranking with Classification Loss for Facial Age Estimation
https://sgvr.kaist.ac.kr/publication/accv2018-age-estimation/
Woobin Im, Sungeun Hong, Sung-Eui Yoon, and Hyun S. Yang
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"
Regional Attention Based Deep Feature for Image Retrieval
https://sgvr.kaist.ac.kr/publication/regional-attention-based-deep-feature-for-image-retrieval/
Jaeyoon Kim and Sung-Eui Yoon
British Machine Vision Conference(BMVC), 2018
Source code: ZIP file, Github page