147 citations · 319 across the 14 of their papers we have counts for
5 papers · 1 filter
LEOD: Label-Efficient Object Detection for Event Cameras
Ziyi Wu, Mathias Gehrig, Qing Lyu +2
Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However, it is costly to fully label event streams for supervised training due to…
Revisiting Token Pruning for Object Detection and Instance Segmentation
Yifei Liu, Mathias Gehrig, Nico Messikommer +2
Vision Transformers (ViTs) have shown impressive performance in computer vision, but their high computational cost, quadratic in the number of tokens, limits their adoption in comp…
From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection
Nikola Zubić, Daniel Gehrig, Mathias Gehrig +1
Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, sele…
Neuromorphic Optical Flow and Real-time Implementation with Event Cameras
Yannick Schnider, Stanislaw Wozniak, Mathias Gehrig +5
Optical flow provides information on relative motion that is an important component in many computer vision pipelines. Neural networks provide high accuracy optical flow, yet their…
A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception
Asude Aydin, Mathias Gehrig, Daniel Gehrig +1
Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse…