1 code implementation • 30 Oct 2024 • Semin Kim, Jaehoon Yoo, Jinwoo Kim, Yeonwoo Cha, Saehoon Kim, Seunghoon Hong
In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data.
1 code implementation • 1 May 2024 • Whie Jung, Jaehoon Yoo, Sungjin Ahn, Seunghoon Hong
Learning compositional representation is a key aspect of object-centric learning as it enables flexible systematic generalization and supports complex visual reasoning.
1 code implementation • CVPR 2023 • Jaehoon Yoo, Semin Kim, Doyup Lee, Chiheon Kim, Seunghoon Hong
However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregressive process.
Ranked #35 on
Video Generation
on UCF-101
2 code implementations • CVPR 2021 • Jinwoo Kim, Jaehoon Yoo, Juho Lee, Seunghoon Hong
Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales.
Ranked #3 on
Point Cloud Generation
on ShapeNet Car