Publications by Weizhe Liu | EPFL Graph Search

Related publications (11)

Human-Centered Scene Understanding via Crowd Counting

Human-centered scene understanding is the process of perceiving and analysing a dynamic scene observed through a network of sensors with emphasis on human-related activities. It includes the visual perception of human-related activities from either single ...

EPFL2021

Counting People by Estimating People Flows

Pascal Fua, Mathieu Salzmann, Weizhe Liu

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

2022

Learning to Align Sequential Actions in the Wild

Pascal Fua, Bugra Tekin, Weizhe Liu

State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspon- dences across videos in time. They either learn frame-to- frame mapping across sequences, which does not leverage temporal information, or a ...

IEEE COMPUTER SOC2022

Context-Aware Crowd Counting

Pascal Fua, Mathieu Salzmann, Weizhe Liu

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. They typically use the same filters over the whole image or over large image patches. Only then do they estimate local scale to compensate for p ...

IEEE/CVF2019

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

Pascal Fua, Weizhe Liu, Martin Pierre Engilberge

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections ...

IEEE COMPUTER SOC2023

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Pascal Fua, Mathieu Salzmann, Weizhe Liu, Krzysztof Maciej Lis

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density in the image plane. While useful for this purpose, this image- plane density has no immediate physical meaning because it is subject to perspecti ...

IEEE2019

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Pascal Fua, Weizhe Liu, Nikita Durasov

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from ...

IEEE COMPUTER SOC2022

Semiautomatic Annotation of MOOC Forum Posts

Pierre Dillenbourg, Lukasz Kidzinski, Weizhe Liu

Massive online open courses' (MOOCs') students who use discussion forums have higher chances of finishing the course. However, little research has been conducted for understanding the underlying factors. One of the reasons which hinders the analysis is the ...

Springer-Verlag Singapore Pte Ltd2016

Semi-automatic annotation of MOOC forum posts

Pierre Dillenbourg, Lukasz Kidzinski, Weizhe Liu

Massive online open courses’ (MOOCs’) students who use discussion forums have higher chances of finishing the course. However, little research has been conducted for understanding the underlying fac- tors. One of the reasons which hinders the analysis is t ...

2015

Estimating People Flows to Better Count Them in Crowded Scenes

Pascal Fua, Mathieu Salzmann, Weizhe Liu

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

2020

Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Pascal Fua, Weizhe Liu, Martin Pierre Engilberge

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections ...

2023