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.
2 code implementations • 5 Mar 2024 • Kumaranage Ravindu Yasas Nagasinghe, Honglu Zhou, Malitha Gunawardhana, Martin Renqiang Min, Daniel Harari, Muhammad Haris Khan
This knowledge, sourced from training procedure plans and structured as a directed weighted graph, equips the agent to better navigate the complexities of step sequencing and its potential variations.
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 • CVPR 2023 • Honglu Zhou, Roberto Martín-Martín, Mubbasir Kapadia, Silvio Savarese, Juan Carlos Niebles
This graph can then be used to generate pseudo labels to train a video representation that encodes the procedural knowledge in a more accessible form to generalize to multiple procedure understanding tasks.
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.
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)
1 code implementation • 22 Dec 2021 • Honglu Zhou, Advith Chegu, Samuel S. Sohn, Zuohui Fu, Gerard de Melo, Mubbasir Kapadia
Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs).
1 code implementation • 11 Dec 2021 • Honglu Zhou, Asim Kadav, Aviv Shamsian, Shijie Geng, Farley Lai, Long Zhao, Ting Liu, Mubbasir Kapadia, Hans Peter Graf
Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects.
Ranked #2 on Group Activity Recognition on Collective Activity
1 code implementation • ICLR 2021 • Honglu Zhou, Asim Kadav, Farley Lai, Alexandru Niculescu-Mizil, Martin Renqiang Min, Mubbasir Kapadia, Hans Peter Graf
We evaluate over CATER dataset and find that Hopper achieves 73. 2% Top-1 accuracy using just 1 FPS by hopping through just a few critical frames.
Ranked #5 on Video Object Tracking on CATER
no code implementations • 8 Dec 2020 • Vahid Azizi, Muhammad Usman, Honglu Zhou, Petros Faloutsos, Mubbasir Kapadia
We present a floorplan embedding technique that uses an attributed graph to represent the geometric information as well as design semantics and behavioral features of the inhabitants as node and edge attributes.
1 code implementation • 9 Oct 2020 • Honglu Zhou, Hareesh Ravi, Carlos M. Muniz, Vahid Azizi, Linda Ness, Gerard de Melo, Mubbasir Kapadia
Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform.
1 code implementation • 6 Jul 2020 • Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, Yongfeng Zhang
Current research on recommender systems mostly focuses on matching users with proper items based on user interests.
2 code implementations • 19 Apr 2020 • Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, Mubbasir Kapadia
In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.
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.