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This paper proposes a novel, computationally efficient regularizer to mitigate event collapse in the CMax framework. From a theoretical point of view, the regularizer is designed based on geometric principles of motion field deformation (measuring area rate of change along point trajectories).

Event cameras offer high temporal resolution and efficiency but remain underutilized in static traffic monitoring. We present eTraM, a first-of-its-kind event-based dataset with 10 hours of traffic data, 2M annotations, and eight participant classes. Evaluated with RVT, RED, and YOLOv8, eTraM highlights the potential of event cameras for real-world applications.

This paper evaluates a dataset using state-of-the-art event-based (RED, RVT) and frame-based (YOLOv8) methods for traffic participant detection tasks and provide baseline benchmarks for assessment. Additionally, the authors conduct experiments to assess the synthetic event-based dataset’s generalization capabilities.

This paper introduces a novel MoE (Mixture of Experts) heat conduction-based object detection algorithm that strikingly balances accuracy and computational efficiency. Initially, we employ a stem network for event data embedding, followed by processing through our innovative MoE-HCO blocks.

This paper presents the first event-camera based egocentric gesture dataset for enabling neuromorphic, low-power solutions for XR-centric gesture recognition.

Unlike traditional SLT based on visible light videos, which is easily affected by factors such as lighting, rapid hand movements, and privacy breaches, this paper proposes the use of high-definition Event streams for SLT, effectively mitigating the aforementioned issues.