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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 Campbell,
Kilian Q Weinberger,
Bharath Hariharan,
Wei-Lun Chao
tl;dr: Diffusion-based point cloud generation to synthesize collaborative driving data.
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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,
Michelle Ramirez,
Elizabeth G Campolongo,
Matthew J Thompson,
Christopher G Lawrence,
Sydne Record,
Neil Rosser,
Anuj Karpatne,
Daniel Rubenstein,
Hilmar Lapp,
Charles V Stewart,
Tanya Berger-Wolf,
Yu Su,
Wei-Lun Chao
tl;dr: A label-efficient fine-grained image segmentation in the biological domain.
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Learning 3D Perception from Others' Predictions
Jinsu Yoo,
Zhenyang Feng,
Tai-Yu Pan,
Yihong Sun,
Cheng Perng Phoo,
Xiangyu Chen,
Mark Campbell,
Kilian Q Weinberger,
Bharath Hariharan,
Wei-Lun Chao
tl;dr: A new way to build 3D object detectors: learning from the predictions of nearby agents.
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Looking Beyond Input Frames: Self-Supervised Adaptation for Video Super-Resolution
Jinsu Yoo,
Jihoon Nam,
Sungyong Baik,
Tae Hyun Kim
tl;dr: Restored video frames can be used as pseudo-labels during test time.
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Semantic-Aware Dynamic Parameter for Video Inpainting Transformer
Eunhye Lee*,
Jinsu Yoo*,
Yunjeong Yang,
Sungyong Baik,
Tae Hyun Kim
tl;dr: Combining semantic maps with video inpainting helps produce better results.
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Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
Jinsu Yoo,
Taehoon Kim,
Sihaeng Lee,
Seung Hwan Kim,
Honglak Lee,
Tae Hyun Kim
tl;dr: Leveraging CNN and ViT features together gives better results for image restoration.
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Fully Convolutional Transformer with Local-Global Attention
Eojindl Lee*,
Sihaeng Lee*,
Janghyeon Lee,
Jinsu Yoo,
Honglak Lee,
Seung Hwan Kim
tl;dr: Flexible and versatile attention mechanism for dense prediction.
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Fast Adaptation to Super-Resolution Networks via Meta-Learning
Seobin Park*,
Jinsu Yoo*,
Donghyeon Cho,
Jiwon Kim,
Tae Hyun Kim
tl;dr: Meta-learning the SR networks allows the model to adapt efficiently to each test image.
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Conference Reviewer: CVPR, ICCV, ECCV, WACV, ACCV
🏆 Outstanding Reviewer in ECCV 2022, 2024
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🏃 I enjoy running. In my free time, I tend to run on a treadmill and have occasionally participated in local marathons. Someday I hope to complete an entire six stars (Tokyo, Boston, London, Berlin, Chicago, and NYC)! Here are my (selected) records so far:
Half 1:59:37 (Seoul, 2016), 10km 54:58 (Seoul, 2023), 10km 57:20 (Hot Chocolate Run - Columbus, 2023)
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