26 citations · 71 across the 39 of their papers we have counts for
5 papers · 1 filter
Multi-task Fusion for Efficient Panoptic-Part Segmentation
Sravan Kumar Jagadeesh, René Schuster, Didier Stricker
In this paper, we introduce a novel network that generates semantic, instance, and part segmentation using a shared encoder and effectively fuses them to achieve panoptic-part segm…
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time
Michael Fürst, Priyash Bhugra, René Schuster +1
Object permanence is the concept that objects do not suddenly disappear in the physical world. Humans understand this concept at young ages and know that another person is still th…
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation
Dipam Goswami, René Schuster, Joost van de Weijer +1
In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift. Although…
Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences
Katharina Bendig, René Schuster, Didier Stricker
In recent years, deep neural networks showed their exceeding capabilities in addressing many computer vision tasks including scene flow prediction. However, most of the advances ar…
RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds
Ramy Battrawy, René Schuster, Mohammad-Ali Nikouei Mahani +1
The proposed RMS-FlowNet is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation which can operate on point clouds of high density. For h…