5 citations · 10 across the 9 of their papers we have counts for
9 papers
Cooperative Automated Driving for Bottleneck Scenarios in Mixed Traffic
M. V. Baumann, J. Beyerer, H. S. Buck +10
Connected automated vehicles (CAV), which incorporate vehicle-to-vehicle (V2V) communication into their motion planning, are expected to provide a wide range of benefits for indivi…
DiffAnt: Diffusion Models for Action Anticipation
Zeyun Zhong, Chengzhi Wu, Manuel Martin +3
Anticipating future actions is inherently uncertain. Given an observed video segment containing ongoing actions, multiple subsequent actions can plausibly follow. This uncertainty…
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly
Chengzhi Wu, Xuelei Bi, Julius Pfrommer +3
On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossib…
SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-task Learning
Chengzhi Wu, Linxi Qiu, Kanran Zhou +2
In this paper, we develop a novel benchmark suite including both a 2D synthetic image dataset and a 3D synthetic point cloud dataset. Our work is a sub-task in the framework of a r…
Self-Supervised Generative-Contrastive Learning of Multi-Modal Euclidean Input for 3D Shape Latent Representations: A Dynamic Switching Approach
Chengzhi Wu, Julius Pfrommer, Mingyuan Zhou +1
We propose a combined generative and contrastive neural architecture for learning latent representations of 3D volumetric shapes. The architecture uses two encoder branches for vox…
Efficient Global Occupancy Mapping for Mobile Robots using OpenVDB
Raphael Hagmanns, Thomas Emter, Marvin Grosse-Besselmann +1
In this work we present a fast occupancy map building approach based on the VDB datastructure. Existing log-odds based occupancy mapping systems are often not able to keep up with…
Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation
Zeyun Zhong, David Schneider, Michael Voit +2
Although human action anticipation is a task which is inherently multi-modal, state-of-the-art methods on well known action anticipation datasets leverage this data by applying ens…
Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection
Tobias Kalb, Björn Mauthe, Jürgen Beyerer
Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new class…
Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation
Tobias Kalb, Jürgen Beyerer
Class-incremental learning for semantic segmentation (CiSS) is presently a highly researched field which aims at updating a semantic segmentation model by sequentially learning new…