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2020 – today
- 2024
[j3]Changsuk Oh
, Youngseok Jang, Dongseok Shim, Changhyeon Kim, Junha Kim, H. Jin Kim:
Automatic Pseudo-LiDAR Annotation: Generation of Training Data for 3D Object Detection Networks. IEEE Access 12: 14227-14237 (2024)- [j2]Dongseok Shim, H. Jin Kim:
DIVIDE: Learning a Domain-Invariant Geometric Space for Depth Estimation. IEEE Robotics Autom. Lett. 9(5): 4663-4670 (2024) - [j1]Seungyeon Yoo, Seungwoo Jung, Yunwoo Lee, Dongseok Shim, H. Jin Kim:
Mono-Camera-Only Target Chasing for a Drone in a Dense Environment by Cross-Modal Learning. IEEE Robotics Autom. Lett. 9(8): 7254-7261 (2024) - [i11]Changsuk Oh, Dongseok Shim, Taekbeom Lee, H. Jin Kim:
Object Remover Performance Evaluation Methods using Class-wise Object Removal Images. CoRR abs/2404.11104 (2024) - 2023
- [c7]Dongseok Shim, Seungjae Lee, H. Jin Kim:
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning. ICML 2023: 31489-31503 - [c6]Dongseok Shim, H. Jin Kim:
SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Network. ICRA 2023: 4983-4990 - [c5]Jeongjun Choi, Dongseok Shim, H. Jin Kim:
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model. IROS 2023: 3773-3780 - [i10]Dongseok Shim, H. Jin Kim:
SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Network. CoRR abs/2301.06715 (2023) - [i9]Dongseok Shim, Seungjae Lee, H. Jin Kim:
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning. CoRR abs/2301.11520 (2023) - [i8]Changsuk Oh, Dongseok Shim, H. Jin Kim:
AURA : Automatic Mask Generator using Randomized Input Sampling for Object Removal. CoRR abs/2305.07857 (2023) - 2022
- [c4]Andreas Lugmayr, Martin Danelljan, Radu Timofte, Kang-Wook Kim, Younggeun Kim, Jae-Young Lee, Zechao Li, Jinshan Pan, Dongseok Shim, Ki-Ung Song, Jinhui Tang, Cong Wang, Zhihao Zhao:
NTIRE 2022 Challenge on Learning the Super-Resolution Space. CVPR Workshops 2022: 785-796 - [c3]Ki-Ung Song, Dongseok Shim, Kang-Wook Kim, Jae-Young Lee, Younggeun Kim:
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow. CVPR Workshops 2022: 967-976 - [c2]Daesol Cho, Dongseok Shim, H. Jin Kim:
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning. NeurIPS 2022 - [i7]Ki-Ung Song, Dongseok Shim, Kang-Wook Kim, Jae Young Lee, Younggeun Kim:
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow. CoRR abs/2204.09679 (2022) - [i6]Daesol Cho, Dongseok Shim, H. Jin Kim:
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning. CoRR abs/2209.15256 (2022) - [i5]Jeongjun Choi, Dongseok Shim, H. Jin Kim:
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model. CoRR abs/2212.02796 (2022) - 2021
- [c1]Dongseok Shim, H. Jin Kim:
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss. ICRA 2021: 13634-13640 - [i4]Dongseok Shim, H. Jin Kim:
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss. CoRR abs/2103.05902 (2021) - 2020
- [i3]Boseong Felipe Jeon, Dongseok Shim, H. Jin Kim:
Detection-Aware Trajectory Generation for a Drone Cinematographer. CoRR abs/2009.01565 (2020) - [i2]Dongseok Shim, H. Jin Kim:
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss. CoRR abs/2011.03207 (2020) - [i1]Dongseok Shim, H. Jin Kim:
Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning. CoRR abs/2011.06190 (2020)
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