Seunghoon Hong

Seunghoon Hong

KAIST

H-index: 25

Asia-South Korea

About Seunghoon Hong

Seunghoon Hong, With an exceptional h-index of 25 and a recent h-index of 23 (since 2020), a distinguished researcher at KAIST, specializes in the field of Computer Vision, Machine Learning, Deep Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Learning to Compose: Improving Object Centric Learning by Injecting Compositionality

Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild

Learning probabilistic symmetrization for architecture agnostic equivariance

Learning Symmetrization for Equivariance with Orbit Distance Minimization

3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation

Information-theoretic state space model for multi-view reinforcement learning

Universal few-shot learning of dense prediction tasks with visual token matching

Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers

Seunghoon Hong Information

University

Position

Assistant Professor

Citations(all)

9852

Citations(since 2020)

7422

Cited By

6954

hIndex(all)

25

hIndex(since 2020)

23

i10Index(all)

30

i10Index(since 2020)

27

Email

University Profile Page

Google Scholar

Seunghoon Hong Skills & Research Interests

Computer Vision

Machine Learning

Deep Learning

Top articles of Seunghoon Hong

Learning to Compose: Improving Object Centric Learning by Injecting Compositionality

2024

Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild

arXiv preprint arXiv:2404.18459

2024/4/29

Learning probabilistic symmetrization for architecture agnostic equivariance

Advances in Neural Information Processing Systems

2024/2/13

Learning Symmetrization for Equivariance with Orbit Distance Minimization

arXiv preprint arXiv:2311.07143

2023/11/13

3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation

arXiv preprint arXiv:2309.04062

2023/9/8

Information-theoretic state space model for multi-view reinforcement learning

2023/6/15

Universal few-shot learning of dense prediction tasks with visual token matching

arXiv preprint arXiv:2303.14969

2023/3/27

Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers

2023

Equivariant hypergraph neural networks

2022/10/23

Diverse generative perturbations on attention space for transferable adversarial attacks

2022/10/16

Seunghoon Hong
Seunghoon Hong

H-Index: 17

Sung-Eui Yoon
Sung-Eui Yoon

H-Index: 19

Learning economic indicators by aggregating multi-level geospatial information

Proceedings of the AAAI Conference on Artificial Intelligence

2022/6/28

Learning continuous representation of audio for arbitrary scale super resolution

2022/5/23

Seunghoon Hong
Seunghoon Hong

H-Index: 17

Jungseul Ok
Jungseul Ok

H-Index: 9

MetaDTA: meta-learning-based drug-target binding affinity prediction

2022/4/5

Learning to generate novel classes for deep metric learning

arXiv preprint arXiv:2201.01008

2022/1/4

Diverse Generative Adversarial Perturbations on Attention Space for Transferable Adversarial Attacks

2022

Seunghoon Hong
Seunghoon Hong

H-Index: 17

Sung-Eui Yoon
Sung-Eui Yoon

H-Index: 19

Part-based pseudo label refinement for unsupervised person re-identification

2022

Seunghoon Hong
Seunghoon Hong

H-Index: 17

Sung-Eui Yoon
Sung-Eui Yoon

H-Index: 19

Transformers meet stochastic block models: attention with data-adaptive sparsity and cost

Advances in Neural Information Processing Systems

2022/12/6

Pure transformers are powerful graph learners

Advances in Neural Information Processing Systems

2022/12/6

Multi-view representation learning via total correlation objective

Advances in Neural Information Processing Systems

2021/12/6

Transformers generalize deepsets and can be extended to graphs & hypergraphs

Advances in Neural Information Processing Systems

2021/12/6

See List of Professors in Seunghoon Hong University(KAIST)

Co-Authors

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