Search Results for author: Hunsang Lee

Found 5 papers, 2 papers with code

Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning

no code implementations CVPR 2023 Hyesong Choi, Hunsang Lee, Wonil Song, Sangryul Jeon, Kwanghoon Sohn, Dongbo Min

Recent vision-based reinforcement learning (RL) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies.

Atari Games reinforcement-learning +3

Environment Agnostic Representation for Visual Reinforcement Learning

1 code implementation ICCV 2023 Hyesong Choi, Hunsang Lee, Seongwon Jeong, Dongbo Min

Generalization capability of vision-based deep reinforcement learning (RL) is indispensable to deal with dynamic environment changes that exist in visual observations.

Domain Generalization reinforcement-learning +1

KNN Local Attention for Image Restoration

no code implementations CVPR 2022 Hunsang Lee, Hyesong Choi, Kwanghoon Sohn, Dongbo Min

In this way, the pair-wise operation establishes non-local connectivity while maintaining the desired properties of the local attention, i. e., inductive bias of locality and linear complexity to input resolution.

Deblurring Image Denoising +3

Self-Supervised Structured Representations for Deep Reinforcement Learning

no code implementations29 Sep 2021 Hyesong Choi, Hunsang Lee, Wonil Song, Sangryul Jeon, Kwanghoon Sohn, Dongbo Min

The proposed method imposes similarity constraints on the three latent volumes; warped query representations by estimated flows, predicted target representations from the transition model, and target representations of future state.

Atari Games Image Reconstruction +3

Adaptive confidence thresholding for monocular depth estimation

1 code implementation ICCV 2021 Hyesong Choi, Hunsang Lee, Sunkyung Kim, Sunok Kim, Seungryong Kim, Kwanghoon Sohn, Dongbo Min

To cope with the prediction error of the confidence map itself, we also leverage the threshold network that learns the threshold dynamically conditioned on the pseudo depth maps.

Monocular Depth Estimation Stereo Matching

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