1 code implementation • ECCV 2020 • Samuel S. Sohn, Honglu Zhou, Seonghyeon Moon, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia
Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning.
no code implementations • 24 Mar 2024 • Che-Jui Chang, Danrui Li, Seonghyeon Moon, Mubbasir Kapadia
In addition, our study of the impact of synthetic data distributions on downstream performance reveals the importance of flexible data generators in narrowing domain gaps for improved model adaptability.
no code implementations • 29 Jun 2023 • Che-Jui Chang, Danrui Li, Deep Patel, Parth Goel, Honglu Zhou, Seonghyeon Moon, Samuel S. Sohn, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia
The study of complex human interactions and group activities has become a focal point in human-centric computer vision.
1 code implementation • ICCV 2023 • Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou, Sejong Yoon, Vladimir Pavlovic, Muhammad Haris Khan, Mubbasir Kapadia
To extract information relevant to the target class, a dominant approach in best-performing FSS methods removes background features using a support mask.
no code implementations • 2 Nov 2022 • Gang Qiao, Kaidong Hu, Seonghyeon Moon, Samuel S. Sohn, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic
Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection and parameter tuning.
1 code implementation • 24 Mar 2022 • Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou, Sejong Yoon, Vladimir Pavlovic, Muhammad Haris Khan, Mubbasir Kapadia
A fundamental limitation of FM is the inability to preserve the fine-grained spatial details that affect the accuracy of segmentation mask, especially for small target objects.
Ranked #5 on Few-Shot Semantic Segmentation on FSS-1000 (1-shot)
no code implementations • CVPR 2022 • Mihee Lee, Samuel S. Sohn, Seonghyeon Moon, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic
Accurate long-term trajectory prediction in complex scenes, where multiple agents (e. g., pedestrians or vehicles) interact with each other and the environment while attempting to accomplish diverse and often unknown goals, is a challenging stochastic forecasting problem.
no code implementations • 13 Oct 2019 • Samuel S. Sohn, Seonghyeon Moon, Honglu Zhou, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia
In this paper, we propose an approach to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments.
no code implementations • 25 Sep 2019 • Glen Berseth, Brandon haworth, Seonghyeon Moon, Mubbasir Kapadia, Petros Faloutsos
Multi-agent reinforcement learning is a particularly challenging problem.