Fast Adaptation to Super-Resolution Networks via Meta-learning. - Seobin Park; Jinsu Yoo; Donghyeon Cho; Jiwon Kim; Tae Hyun Kim 006 (ECCV (27), 2020)

Self-Supervised Adaptation for Video Super-Resolution. - Jinsu Yoo; Tae Hyun Kim 006 (CoRR, 2021)

Fully Convolutional Transformer with Local-Global Attention. - Sihaeng Lee; Eojindl Yi; Janghyeon Lee 001; Jinsu Yoo; Honglak Lee; Seung Hwan Kim (IROS, 2022)

Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution. - Jinsu Yoo; Taehoon Kim; Sihaeng Lee; Seung Hwan Kim; Honglak Lee; Tae Hyun Kim 006 (CoRR, 2022)

Semantic-Aware Dynamic Parameter for Video Inpainting Transformer. - Eunhye Lee; Jinsu Yoo; Yunjeong Yang; Sungyong Baik; Tae Hyun Kim 006 (ICCV, 2023)

Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution. - Jinsu Yoo; Taehoon Kim; Sihaeng Lee; Seung Hwan Kim; Honglak Lee; Tae Hyun Kim 006 (WACV, 2023)

Looking beyond input frames: Self-supervised adaptation for video super-resolution. - Jinsu Yoo; Jihoon Nam; Sungyong Baik; Tae Hyun Kim 006 (Pattern Recognit., 2024)

Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene. - Tai-Yu Pan; Sooyoung Jeon; Mengdi Fan; Jinsu Yoo; Zhenyang Feng; Mark E. Campbell; Kilian Q. Weinberger; Bharath Hariharan; Wei-Lun Chao (CVPR, 2025)

Learning 3D Perception from Others' Predictions. - Jinsu Yoo; Zhenyang Feng; Tai-Yu Pan; Yihong Sun; Cheng Perng Phoo; Xiangyu Chen 007; Mark E. Campbell; Kilian Q. Weinberger; Bharath Hariharan; Wei-Lun Chao (ICLR, 2025)

Static Segmentation by Tracking: A Frustratingly Label-Efficient Approach to Fine-Grained Segmentation. - Zhenyang Feng; Zihe Wang; Saul Ibaven Bueno; Tomasz Frelek; Advikaa Ramesh; Jingyan Bai; Lemeng Wang; Zanming Huang; Jianyang Gu; Jinsu Yoo; Tai-Yu Pan; Arpita Chowdhury; et al. (CoRR, 2025)

Leveraging Sparse LiDAR for RAFT-Stereo: A Depth Pre-Fill Perspective. - Jinsu Yoo; Sooyoung Jeon; Zanming Huang; Tai-Yu Pan; Wei-Lun Chao (CoRR, 2025)