4 accepted papers
When applied sequentially to video, frame-based networks often exhibit temporal inconsistency—for example, outputs that flicker between frames. This problem is amplified when the network inputs contain time-varying corruptions. In this work, we introduce a general approach for adapting frame-based m…
Event cameras capture the world at high time resolution and with minimal bandwidth requirements. However event streams which only encode changes in brightness do not contain sufficient scene information to support a wide variety of downstream tasks. In this work we design generalized event cameras t…
Vision Transformers achieve impressive accuracy across a range of visual recognition tasks. Unfortunately, their accuracy frequently comes with high computational costs. This is a particular issue in video recognition, where models are often applied repeatedly across frames or temporal chunks. In th…
"Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated. Such redundancy occurs at multiple levels of complexity, from low-level pixel values to textures and high-level semantics. We propose Event Neural Networks (EvNets), which leverage this red…