activity
20192023
most citedUPAR: Unified Pedestrian Attribute Recognition and Person Retrieval

2 citations · 2 across the 2 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2023

Security Fence Inspection at Airports Using Object Detection

Nils Friederich, Andreas Specker, Jürgen Beyerer

To ensure the security of airports, it is essential to protect the airside from unauthorized access. For this purpose, security fences are commonly used, but they require regular i…

cs.CV2023

A Survey on Deep Learning Techniques for Action Anticipation

Zeyun Zhong, Manuel Martin, Michael Voit +2

The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numer…

cs.CV2023

Attention-based Part Assembly for 3D Volumetric Shape Modeling

Chengzhi Wu, Junwei Zheng, Julius Pfrommer +1

Modeling a 3D volumetric shape as an assembly of decomposed shape parts is much more challenging, but semantically more valuable than direct reconstruction from a full shape repres…

cs.CV20233 cited

Attention-based Point Cloud Edge Sampling

Chengzhi Wu, Junwei Zheng, Julius Pfrommer +1

Point cloud sampling is a less explored research topic for this data representation. The most commonly used sampling methods are still classical random sampling and farthest point…

cs.CV20222 cited

UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval

Andreas Specker, Mickael Cormier, Jürgen Beyerer

Recognizing soft-biometric pedestrian attributes is essential in video surveillance and fashion retrieval. Recent works show promising results on single datasets. Nevertheless, the…

cs.CV2019

Human Pose Estimation for Real-World Crowded Scenarios

Thomas Golda, Tobias Kalb, Arne Schumann +1

Human pose estimation has recently made significant progress with the adoption of deep convolutional neural networks. Its many applications have attracted tremendous interest in re…