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arXiv:2609.17134v1 Announce Type: new Abstract: Neuromorphic vision systems operate under strict constraints on bandwidth, memory, and energy, particularly at the edge, motivating early mechanisms fo...
The paper introduces a confidence‑normalized continuous multi‑timescale representation for event cameras, using logarithmic B‑spline temporal encoding and a geometry‑aware local confidence mechanism. When paired with a fixed feed‑forward EventCenterNet detector, this representation outperforms the compact CSTR representation on the PEDRo and Gen1 datasets. Additionally, a recursive exponential‑polynomial approximation is proposed to allow efficient event‑by‑event updates while maintaining detection performance.
arXiv:2610.41834v1 Announce Type: new Abstract: Accurate BEV map prediction is a critical component of autonomous driving systems, enabling downstream planning and control. Recent approaches such as...
The paper introduces Spatially‑Sparse Linear Attention (SSLA), a novel attention mechanism that activates only a sparse subset of spatial states, enabling efficient parallel training and inference for event‑based vision. Building on SSLA, the authors present SSLA‑Det, an end‑to‑end asynchronous linear attention model that achieves state‑of‑the‑art accuracy on Gen1 and N‑Caltech101 while reducing per‑event computation by more than 20× compared to the strongest prior asynchronous baseline.
arXiv:2610.09873v1 Announce Type: new Abstract: Binary Neural Networks (BNNs) enable efficient deep learning deployment on resource constrained devices with weights and activations compressed to one...
arXiv:2603. 28251v3 Announce Type: replace-cross Abstract: Drivers' visual attention provides critical cues for anticipating latent hazards and directly shapes decision-making and control maneuvers, where its absence can compromise traffic safety.