Sejong Yoon

Sejong Yoon

The College of New Jersey

H-index: 12

North America-United States

About Sejong Yoon

Sejong Yoon, With an exceptional h-index of 12 and a recent h-index of 9 (since 2020), a distinguished researcher at The College of New Jersey, specializes in the field of Artificial Intelligence, Machine Learning, Visual Computing.

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

Microscopic modeling of attention-based movement behaviors

MiRODES: Mini Intelligent Robot for On-campus Domain-specific Event Support

Toward Realistic Human Crowd Simulations with Data-Driven Parameter Space Exploration

Learning from Synthetic Human Group Activities

Msi: maximize support-set information for few-shot segmentation

An information-theoretic approach for estimating scenario generalization in crowd motion prediction

Hm: Hybrid masking for few-shot segmentation

A2X: An end-to-end framework for assessing agent and environment interactions in multimodal human trajectory prediction

Sejong Yoon Information

University

Position

___

Citations(all)

440

Citations(since 2020)

263

Cited By

268

hIndex(all)

12

hIndex(since 2020)

9

i10Index(all)

14

i10Index(since 2020)

8

Email

University Profile Page

Google Scholar

Sejong Yoon Skills & Research Interests

Artificial Intelligence

Machine Learning

Visual Computing

Top articles of Sejong Yoon

Microscopic modeling of attention-based movement behaviors

Transportation Research Part C: Emerging Technologies

2024/5/1

Sejong Yoon
Sejong Yoon

H-Index: 8

Mubbasir Kapadia
Mubbasir Kapadia

H-Index: 27

MiRODES: Mini Intelligent Robot for On-campus Domain-specific Event Support

2024/3/11

Sejong Yoon
Sejong Yoon

H-Index: 8

Toward Realistic Human Crowd Simulations with Data-Driven Parameter Space Exploration

2024/1/17

Learning from Synthetic Human Group Activities

arXiv preprint arXiv:2306.16772

2023/6/29

Msi: maximize support-set information for few-shot segmentation

2023

An information-theoretic approach for estimating scenario generalization in crowd motion prediction

arXiv preprint arXiv:2211.00817

2022/11/2

Hm: Hybrid masking for few-shot segmentation

2022/10/20

A2X: An end-to-end framework for assessing agent and environment interactions in multimodal human trajectory prediction

Computers & Graphics

2022/8/1

Harnessing fourier isovists and geodesic interaction for long-term crowd flow prediction

2022

Muse-vae: Multi-scale vae for environment-aware long term trajectory prediction

2022

A2x: An agent and environment interaction benchmark for multimodal human trajectory prediction

2021/11/10

Constructivist approaches for computational emotions: A systematic survey

2021/11/4

Sejong Yoon
Sejong Yoon

H-Index: 8

Predicting crowd egress and environment relationships to support building design optimization

Computers & Graphics

2020/5/1

Laying the foundations of deep long-term crowd flow prediction

Proceedings of the European Conference on Computer Vision (ECCV)

2020

See List of Professors in Sejong Yoon University(The College of New Jersey)

Co-Authors

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