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I am currently a MS/PhD student at Seoul National University, advised by Byoung-Tak Zhang . I earned my Bachelor's degree in Department of Computer Science and Engineering from Seoul National University. I'm interested in self-supervised representation learning and generative models in the vision domain, and applications to robotics. |
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Won-Seok Choi, Hyundo Lee, Dong-Sig Han, Junseok Park, Heeyeon Koo, Byoung-Tak Zhang We propose an active data filtering process during self-supervised pre-training in our novel framework, Duplicate Elimination (DUEL). This framework integrates an active memory inspired by human working memory and introduces distinctiveness information. |
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Hyundo Lee, Inwoo Hwang, Hyunsung Go, Won-Seok Choi, Kibeom Kim, Byoung-Tak Zhang Inspired by human behavior that depicts an image by sketching, we propose a novel representation learning framework that captures geometric information of the scene, such as distance or shape. |
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Dong-Sig Han, Hyunseo Kim, Hyundo Lee, JeHwan Ryu, Byoung-Tak Zhang Inspired by a first-order optimization method called mirror descent, this paper proposes to predict a sequence of reward functions, which are iterative solutions for a constrained convex problem. |
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Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J Lim, Byoung-Tak Zhang We propose Message Passing Adaptive Resonance Theory (MPART) that learns the distribution and topology of input data online. Through message passing on the topological graph, MPART actively queries informative and representative samples, and continuously improves the classification performance using both labeled and unlabeled data. |
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Chung-Yeon Lee, Hyundo Lee, Injune Hwang, Byoung-Tak Zhang We present a visual perception framework for an intelligent mobile robot. Based on the robot operating system middleware, our framework integrates a broad set of advanced algorithms capable of recognising people, objects and human poses, as well as describing observed scenes. |
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Chung-Yeon Lee, Hyundo Lee, Injune Hwang, Byoung-Tak Zhang We present a spatial perception framework that uses an object-aware visual scene representation to enhance the spatial abilities. The proposed representation compensates for aberrations of conventional geometric scene representations by fusing those representations with semantic features extracted from perceived objects. |
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Jiseob Kim, Seungjae Jung, Hyundo Lee, Byoung-Tak Zhang We present an encoder-powered generative adversarial network (EncGAN) that is able to learn both the multi-manifold structure and the abstract features of data. |
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