25 citations · 49 across the 9 of their papers we have counts for
9 papers · 1 filter
Multi-view Tracking Using Weakly Supervised Human Motion Prediction
Martin Engilberge, Weizhe Liu, Pascal Fua
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 paradi…
Learning to Align Sequential Actions in the Wild
Weizhe Liu, Bugra Tekin, Huseyin Coskun +3
State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspondences across videos in time. They either learn frame-to-frame map…
Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning
Weizhe Liu, David Ferstl, Samuel Schulter +3
We introduce a novel approach to unsupervised and semi-supervised domain adaptation for semantic segmentation. Unlike many earlier methods that rely on adversarial learning for fea…
Leveraging Self-Supervision for Cross-Domain Crowd Counting
Weizhe Liu, Nikita Durasov, Pascal Fua
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 o…
Counting People by Estimating People Flows
Weizhe Liu, Mathieu Salzmann, Pascal Fua
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 cons…
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting
Weizhe Liu, Mathieu Salzmann, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, deep learning approaches are vulnerable to adversar…
Estimating People Flows to Better Count Them in Crowded Scenes
Weizhe Liu, Mathieu Salzmann, Pascal Fua
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 cons…
Context-Aware Crowd Counting
Weizhe Liu, Mathieu Salzmann, Pascal Fua
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 lar…
Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation
Weizhe Liu, Krzysztof Lis, Mathieu Salzmann +1
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 d…