From the 1 of 10 linked papers with an AI index.
10 papers
Sequence-SOD: Bio-inspired Sequence-aware Spiking ObjectDetection for Event Cameras
Katharina Bendig, René Schuster, Didier Stricker
The paper presents Sequence-SOD, a spiking neural network object detector for event cameras that processes continuous sequences of events while preserving membrane potentials, lead…
MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
Shishir Muralidhara, Didier Stricker, René Schuster +1
Continual semantic segmentation requires models to adapt to new domains or modalities without sacrificing performance on previously learned tasks. Expert-based learning, in which t…
Sensor Generalization for Adaptive Sensing in Event-based Object Detection via Joint Distribution Training
Aheli Saha, René Schuster, Didier Stricker
Bio-inspired event cameras have recently attracted significant research due to their asynchronous and low-latency capabilities. These features provide a high dynamic range and sign…
LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration
Aditya Ranjan Dash, Ramy Battrawy, René Schuster +1
Advanced autonomous systems rely on multi-sensor fusion for safer and more robust perception. To enable effective fusion, calibrating directly from natural driving scenes (i.e., ta…
SF3D-RGB: Scene Flow Estimation from Monocular Camera and Sparse LiDAR
Rajai Alhimdiat, Ramy Battrawy, René Schuster +2
Scene flow estimation is an extremely important task in computer vision to support the perception of dynamic changes in the scene. For robust scene flow, learning-based approaches…
SAILS: Segment Anything with Incrementally Learned Semantics for Task-Invariant and Training-Free Continual Learning
Shishir Muralidhara, Didier Stricker, René Schuster
Continual learning remains constrained by the need for repeated retraining, high computational costs, and the persistent challenge of forgetting. These factors significantly limit…