1.4k citations · 4.7k across the 67 of their papers we have counts for
88 papers
MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts
Matthias Neuwirth-Trapp, Maarten Bieshaar, Danda Paudel +3
Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the c…
Towards accurate instance segmentation in large-scale LiDAR point clouds
Binbin Xiang, Torben Peters, Theodora Kontogianni +4
Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object…
SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory
Han Sun, Rui Gong, Konrad Schindler +1
Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain. Prior works typically…
AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation
Yuanwen Yue, Sabarinath Mahadevan, Jonas Schult +4
During interactive segmentation, a model and a user work together to delineate objects of interest in a 3D point cloud. In an iterative process, the model assigns each data point t…
Uncertainty Voting Ensemble for Imbalanced Deep Regression
Yuchang Jiang, Vivien Sainte Fare Garnot, Konrad Schindler +1
Data imbalance is ubiquitous when applying machine learning to real-world problems, particularly regression problems. If training data are imbalanced, the learning is dominated by…
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
Corinne Stucker, Vivien Sainte Fare Garnot, Konrad Schindler
Satellite image time series in the optical and infrared spectrum suffer from frequent data gaps due to cloud cover, cloud shadows, and temporary sensor outages. It has been a long-…