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연구

Research & Laboratory

제목
세미나 [09/04] Learning Weakly-supervised Semantic Correspondence and Its Applications
작성일
2019.08.30
작성자
전기전자공학부
게시글 내용

< BK21 플러스 BEST 정보기술 사업단 세미나 개최 안내 >


개최일시 : 2019년 9월 4일 (수) 16:00 ~ 17:00

개최장소 : 제 3공학관 C716호

세미나 제목 : Learning Weakly-supervised Semantic Correspondence and Its Applications

내용 :

Establishing dense semantic correspondences across semantically similar images is essential for numerous computer vision, machine learning, and computation photography applications. Unlike traditional dense correspondence for estimating depth or optical flow, semantic correspondence poses additional challenges due to intra-class appearance and shape variations among different instances within the same object category. In addition, the lack of an appropriate benchmark with dense ground-truth correspondences make supervised learning less feasible for this task. In this talk, we first investigate state-of-the-art research on semantic correspondence that leverages deep convolutional neural networks (CNNs) in a weakly supervised fashion, called recurrent transformer networks (RTNs). We then investigate interesting its applications such as photorealistic style transfer and landmark detection that can be jointly solved with semantic correspondence. Finally, other potential applications and further directions will be presented.



강연자 성함&직함 / 소속 : 김승룡 & Post-Doctoral Researcher / École Polytechnique Fédérale de Lausanne (EPFL)

초청자 : 전기전자공학과 교수 손광훈