In the realm of video object tracking auxiliary modalities such as depth thermal or event data
have emerged as valuable assets to complement the RGB trackers. In practice most existing …

Recent advances in event-based vision suggest that they complement traditional cameras
by providing continuous observation without frame rate limitations and high dynamic range …

Learning unsupervised world models for autonomous driving has the potential to improve
the reasoning capabilities of today's systems dramatically. However, most work neglects the …

In this paper we delve into the nuanced challenge of tailoring the Segment Anything Models
(SAMs) for integration with event data with the overarching objective of attaining robust and …

In this paper we explore the problem of event-based meshflow estimation a novel task that
involves predicting a spatially smooth sparse motion field from event cameras. To start we …

Event cameras are dynamic vision sensors inspired by the biological retina, characterized
by their high dynamic range, high temporal resolution, and low power consumption. These …

Single RGB or LiDAR is the mainstream sensor for the challenging scene flow which relies
heavily on visual features to match motion features. Compared with single modality existing …

This paper introduces a novel deep learning model for grape disease detection that
integrates multimodal data and parallel heterogeneous activation functions, significantly …

Autonomous racing has rapidly gained research attention. Traditionally, racing cars rely on
2D LiDAR as their primary visual system. In this work, we explore the integration of an event …

Recently, the task of Video Frame Prediction (VFP), which predicts future video frames from
previous ones through extrapolation, has made remarkable progress. However, the …