Articles in Peer-Reviewed Journals

1. D. Li, M. Schwartz, S. Sohn, S. Yoon, V. Pavlovic, M. Kapadia, “Microscopic Modeling of Attention-based Movement Behaviors,” Transportation Research Part C: Emerging Technologies, Vol. 162, 104583, 2024. [publisher]

2. S. Sohn, M. Lee, S. Moon, G. Qiao, M. Usman, S. Yoon, V. Pavlovic, M. Kapadia, “A2X: An End-to-End Framework for Assessing Agent and Environment Interactions in Multimodal Human Trajectory Prediction,” Computers & Graphics, Vol. 106, Pages 130-140, 2022. [publisher]

3. K. Hu, S. Yoon, V. Pavlovic, P. Faloutsos, M. Kapadia, “Predicting Crowd Egress and Environment Relationships to Support Building Design Optimization,” Computers & Graphics, Vol. 88, Pages 83-96, 2020. [publisher]

4. S. Yoon and S. Kim, “k-Top Scoring Pair Algorithm for Feature Selection in SVM with Applications to Microarray Data Classification,” Soft Computing, 14 (2):151-159, 2010. [publisher]

5. S. Yoon and S. Kim, “Mutual Information-based SVM-RFE for Diagnostic Classification of Digitized Mammograms,” Pattern Recognition Letters, 30 (16):1489-1495, 2009. [publisher]

6. S. Yoon and S. Kim, “AdaBoost-based Multiple SVM-RFE for classification of mammograms in DDSM,” BMC Medical Informatics and Decision Making, 9 (Suppl 1):S1, 2009. [publisher]

Papers in Peer-Reviewed Conference and Workshop Proceedings

1. Q. Liu, D. Li, S. Sohn, S. Yoon, M. Kapadia, V. Pavlovic, “TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction,” 27th International Conference on Pattern Recognition (ICPR), Kolkata, India, 2024. [pdf]

2. C-J. Chang, D. Li, D.A. Patel, P. Goel, S. Moon, S. Sohn, H. Zhou, S. Yoon, V. Pavlovic, M. Kapadia, “Learning from Synthetic Human Group Activities,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2024. [page]

3. S. Moon, S. Sohn, H. Zhou, S. Yoon, V. Pavlovic, M. Haris, M. Kapadia, “MSI: Maximize Support-Set Information for Few-Shot Segmentation,” International Conference on Computer Vision (ICCV), Paris, France, 2023. [page]

4. S. Moon, S. Sohn, H. Zhou, S. Yoon, V. Pavlovic, M. Haris, M. Kapadia, “HM: Hybrid Masking for Few-Shot Segmentation,” The 17th European Conference on Computer Vision (ECCV), Tel Aviv, Israel, 2022. [page]

5. S. Sohn, S. Moon, H. Zhou, M. Lee, S. Yoon, V. Pavlovic, M. Kapadia, “Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction,” The 31st International Joint Conference on Artificial Intelligence (IJCAI), Vienna, Austria, 2022. [page]

6. M. Lee, S. Sohn, S. Moon, S. Yoon, M. Kapadia, V. Pavlovic, “MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022. [page]

7. A. Viola, V. Pavlovic, S. Yoon, “Constructivist Approaches for Computational Emotions: A Systematic Survey,” In: N. Gurney, G. Sukthankar (eds) Computational Theory of Mind for Human-Machine Teams. AAAI-FSS 2021. Lecture Notes in Computer Science, vol 13775. Springer, Cham, 2022. [pdf]

8. S. Sohn, M. Lee, S. Moon, G. Qiao, M. Usman, S. Yoon, V. Pavlovic, M. Kapadia, “A2X: An Agent and Environment Interaction Benchmark for Multimodal Human Trajectory Prediction,” The 14th ACM SIGGRAPH conference on Motion, Interaction and Games (MIG), Online, 2021. [page]

9. S. Sohn, H. Zhou, S. Moon, S. Yoon, V. Pavlovic, M. Kapadia, “Laying the Foundations of Deep Long-Term Crowd Flow Prediction,” The 16th European Conference on Computer Vision (ECCV), Online, 2020. [page]

10. S. Yoon, “PyPlat: A Flexible Platform Game Project,” in T. Neller, et al., “Model AI Assignments 2020,” The 10th Symposium on Educational Advances in Artifical Intelligence (EAAI), New York, NY, USA, 2020. [pdf] [page]

11. G. Qiao, H. Zhu, M. Kapadia, S. Yoon, and V. Pavlovic, “Scenario Generalization of Data-driven Imitation Models in Crowd Simulation,” The 12th ACM SIGGRAPH conference on Motion, Interaction and Games (MIG), Newcastle Upon Tyne, United Kingdom, 2019. [publisher]

12. B. Sang and S. Yoon, “A Neural Network Approach for Birds of a Feather Solvability Prediction,” The 9th Symposium on Educational Advances in Artificial Intelligence (EAAI), Honolulu, HI, USA, 2019. [pdf] [code]

13. S. Yoon and J. Kim, “Object-centric Scene Understanding for Image Memorability Prediction,” The 1st IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR), Miami, FL, USA, 2018. [publisher]

14. G. Qiao, S. Yoon, M. Kapadia, and V. Pavlovic, “The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation,” The 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA, 2018. [pdf]

15. W. Liu, K. Hu, S. Yoon, V. Pavlovic, P. Faloutsos, and M. Kapadia, “Characterizing the Relationship between Environment Layout and Crowd Movement using Machine Learning,” The 10th ACM SIGGRAPH conference on Motion in Games (MIG), Barcelona, Spain, 2017. [pdf]

16. S. Yoon, M. Kapadia, P. Sahu, and V. Pavlovic, “Filling in the Blanks: Reconstructing Microscopic Crowd Motion from Multiple Disparate Noisy Sensors,” 1st Workshop on Computer Vision Applications in Surveillance and Transportation (AVSTI) in conjunction with IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 2016. [publisher]

17. C. Song, S. Yoon, and V. Pavlovic, “Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty,” The 30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, AZ, USA, 2016. [pdf]

18. B. Gholami, S. Yoon, and V. Pavlovic, “Decentralized Approximate Bayesian Inference for Distributed Sensor Network,” The 30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, AZ, USA, 2016. [pdf] [code]

19. S. Yoon and V. Pavlovic, “Sentiment Flow for Video Interestingness Prediction,” The 1st ACM Workshop on Human Centered Event Understanding from Multimedia (HuEvent) in conjunction with ACM International Conference on Multimedia (MM), Orlando, FL, USA, 2014. [pdf] [code]

20. J. Kim, S. Yoon, and V. Pavlovic, “Relative Spatial Features for Image Memorability,” The 21st ACM International Conference on Multimedia (MM), Barcelona, Cataluña, Spain, 2013. [pdf]

21. S. Yoon and V. Pavlovic, “Distributed Probabilistic Learning for Camera Networks with Missing Data,” Advances in Neural Information Processing Systems (NeurIPS) 25, Lake Tahoe, NV, USA, 2012. [pdf] [code]

22. S. Yoon and S. Kim. “Multiple SVM-RFE Using Boosting for Mammogram Classification,” International Joint Conference on Computational Sciences and Optimization (CSO), Sanya, Hainan, China, 2009. [publisher]

23. S. Yoon and S. Kim. “AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM,” IEEE International Conference on Bioinformatics and Biomeidcine Workshops (BIBMW), Philadelphia, PA, USA, 2008. [publisher]

24. S. Kim and S. Yoon. “BI-RADS Features-Based Computer-Aided Diagnosis of Abnormalities in Mammographic Images,” 6th International Special Topic Conference on Information Technology Applications in Biomedicine (ITAB), Tokyo, Japan, 2007. [publisher]

25. S. Kim and S. Yoon. “Mass Lesions Classification in Digital Mammography using Optimal Subset of BI-RADS and Gray Level Features,” 6th International Special Topic Conference on Information Technology Applications in Biomedicine (ITAB), Tokyo, Japan, 2007. [publisher]

26. S. Kim, S. Yoon, and D. Shin. “Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination,” Frontiers in the Convergence of Bioscience and Information Technologies (FBIT), Jeju City, Korea, 2007. [publisher]

Posters and Extended Abstracts in Peer-Reviewed Conference and Workshop Proceedings

1. M. Manzano, K. O’Donnell, E. Espinosa, and S. Yoon, “MiRODES: Mini Intelligent Robot for On-campus Domain-specific Event Support,” in companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI), Boulder, CO, USA, March 11-14, 2024. [publisher] (Student Design Challenge)

2. K. Hu, S. Yoon, V. Pavlovic, M. Kapadia, “Toward Realistic Human Crowd Simulations with Data-Driven Parameter Space Exploration,” in proceedings of 2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR), Los Angeles, CA, USA, January 17-19, 2024. [publisher]

3. A. Viola and S. Yoon, “A Hybrid Approach for Video Memorability Prediction,” in proceedings of MediaEval 2019 Workshop, Sophia Antipolis, France, October 27-30, 2019. [pdf]

4. A. Weiss, B. Sang, and S. Yoon, “Predicting Memorability via Early Fusion Deep Neural Network,” in proceedings of MediaEval 2018 Workshop, Sophia Antipolis, France, October 29-31, 2018. [pdf]

5. S. Yoon, “TCNJ-CS@MediaEval2017: Predicting Media Interestingness,” in proceedings of MediaEval 2017 Workshop in conjunction with Conference and Labs of the Evaluation Forum (CLEF), Dublin, Ireland, September 13-15, 2017. [pdf]

6. S. Yoon, “TCNJ-CS@MediaEval2017: Emotional Impact of Movies,” in proceedings of MediaEval 2017 Workshop in conjunction with Conference and Labs of the Evaluation Forum (CLEF), Dublin, Ireland, September 13-15, 2017. [pdf]

7. B. Gholami, S. Yoon, and V. Pavlovic, “PhD Forum: Mean Field Variational Inference using Bregman ADMM for Distributed Camera Network,” International Conference on Distributed Smart Camera (ICDSC), Seville, Spain, 2015. [publisher]

Other Presentations and Technical Reports

1. G. Qiao, K. Hu, S. Moon, S. Yoon, M. Kapadia, and V. Pavlovic, “Measure Task-Level Inter-Agent Interaction Difficulty in Decentralized Scenarios,” Neuro-Cognitive Modeling of Humans and Environments (NCMHE) in conjunction with International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, January 8, 2021 (online). [pdf]

2. S. Sohn, S. Moon, H. Zhou, S. Yoon, V. Pavlovic, and M. Kapadia, “Deep Crowd-Flow Prediction in Built Environments,” Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop (AI+HADR) in conjunction with Neural Information Processing Systems (NeurIPS), Vancouver, British Columbia, Canada, December 13, 2019. [pdf]

3. S. Yoon and J. Kim, “Improving Image Memorability Prediction via Coarse Scene Parsing,” Vision Meets Cognition Workshop in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017. (abstract)

4. S. Yoon and V. Pavlovic, “Decentralized Probabilistic Learning for Sensor Network,” Symposium on Machine Learning for Characterization of Cognitive Communications and Radar in conjunction with IEEE Global Conference on Signal and Information Processing (GlobalSIP), Greater Washington, D.C., USA, 2016. (invited talk)

5. J. Kim, S. Yoon, and V. Pavlovic, “Relative Spatial Features for Image Memorability,” 3rd GNY Area Multimedia and Vision Meeting, New York, NY, USA, 2013. (abstract)

6. S. Yoon and V. Pavlovic, “Distributed Probabilistic Learning for Camera Networks with Missing Data,” New York Academy of Sciences 7th Annual Machine Learning Symposium, New York, NY, USA, 2012. (abstract)

7. S. Yoon and V. Pavlovic, “Distributed Probabilistic Learning for Camera Networks,” DCS-TR-696, Department of Computer Science, Rutgers University, June 2012. [pdf]

Thesis

• S. Yoon, “Generalized Distributed Learning Under Uncertainty for Camera Networks,” Ph.D. Dissertation, Rutgers, The State University of New Jersey-New Brunswick, October 2016.

• S. Yoon, “Diagnostic Classification of Digitized Mammograms by AdaBoost-based and Mutual Information-based Support Vector Machines with Recursive Feature Elimination,” Masters Thesis, Sogang University, February 2009.