arXiv Computer Vision
Event-Frame Fusion for Inter-Frame Segmentation via Event-Guided Motion
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arXiv Computer Vision
Sep 2
Spiking Patches: Asynchronous, Sparse, and Efficient Tokens for Event Cameras
arXiv:2510.26614v2 Announce Type: replace Abstract: We propose tokenization of events and present a tokenizer, Spiking Patches, specifically designed for event cameras. Given a stream of asynchronous...
arXiv AI
Jun 2
A Survey of 3D Reconstruction with Event Cameras
arXiv:2505. 08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes.
arXiv Computer Vision
Sep 23
LiFR v2: Completion-Augmented Event Propagation for High-Rate Dense Prediction
arXiv:2609.25803v1 Announce Type: new Abstract: High-rate dense perception in dynamic environments is limited by the low update rate of RGB cameras, as rapid scene changes can occur between frames. E...
arXiv Computer Vision
Oct 12
Leveraging Motion Estimation for Efficient Bayer-Domain Video Convolutional Networks
The paper introduces Motion Estimation-based Video Convolution (MEVC), a framework that removes the need for image signal processors by operating directly on raw Bayer pixel data. MEVC incorporates sliding‑window motion estimation into each convolutional layer, allowing prediction and residual refinement to cut redundant computations across frames. The resulting end‑to‑end pipeline achieves more than 70% reduction in FLOPs while maintaining accuracy on video semantic segmentation, depth estimation, and object detection tasks.
arXiv AI
Aug 3
Detecting AI-Generated Videos with Spiking Neural Networks
arXiv:2605. 05895v2 Announce Type: replace-cross Abstract: Modern AI-generated videos are photorealistic at the single-frame level, leaving inter-frame dynamics as the main remaining axis for detection.
arXiv Computer Vision
Sep 15
A 25-$\mu$s/inf Event-driven Graph Neural Network Processor with Spatiotemporal Caching and Spline Convolution for Ultra-low-latency AI at the Edge
The paper introduces ETHEREAL, the first accelerator for event-driven graph neural networks (EV‑GNNs) that can handle 640×480 resolution inputs. It achieves this through a neighbor‑parallel spline convolution engine and a 2D/3D‑split memory hierarchy that includes a novel region‑of‑interest spatiotemporal caching mechanism. Measurements show end‑to‑end inference latency of 25.6 µs and energy consumption of 1.7 µJ per event on state‑of‑the‑art workloads.