Event-Driven Graph Neural Network Accelerators for Low-Power Vision

Event-based cameras promise new opportunities for smart vision systems deployed at the edge. Contrary to their conventional frame-based counterparts, event-based cameras generate temporal light intensity changes as events on a per-pixel basis, enabling ultra-low latency with microsecond-scale temporal resolution, low power consumption at...

3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network

This paper presents a sparse Change-Based Convolutional Long Short-Term Memory (CB-ConvLSTM) model for event-based eye tracking, key for next-generation wearable healthcare technology such as AR/VR headsets. We leverage the benefits of retina-inspired event cameras, namely their low-latency response and sparse output event stream, over...

EV-Mask-RCNN: Instance Segmentation in Event-based Videos

Instance segmentation on data from Dynamic Vision Sensors (DVS) is an important computer vision task that needs to be tackled in order to push the research forward on these types of inputs. This paper aims to show that deep learning based techniques can be used to solve the task of instance segmentation on DVS data. A high performing model was...