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.
arXiv:2607. 22714v1 Announce Type: cross Abstract: Real-time perception is a foundational requirement for advanced driver assistance systems (ADAS) and autonomous vehicles, yet embedded automotive platforms impose severe constraints on compute, memory, and power.
DiDA introduces a lightweight video object segmentation framework that leverages Distillation Learning of Deformable Attention. The method uses deformable attention to adapt key and value positions across frames, enabling object representations that are responsive to spatial and temporal changes. Experiments on DAVIS and YouTube‑VOS benchmarks show state‑of‑the‑art performance and efficient memory usage.
arXiv:2607.05205v2 Announce Type: replace Abstract: Fast and reliable motion detection is essential for machine vision and autonomous systems operating in dynamic environments. This work integrates e...
arXiv:2509.22650v3 Announce Type: replace Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,...
PPS: Plug-and-Play Saccadic Vision for Fine-Grained Classification proposes a lightweight extension that transforms any pretrained backbone with spatial feature maps into a saccadic classifier. The method adds only a small priority head and a single cross-attention fusion block, using a weight-tied encoder to process a downsampled peripheral glance and high-resolution glimpse crops selected by a priority distribution. A sequential sampler with non-maximum suppression draws a configurable number of glimpses, and a residual multi-head cross-attention block fuses the glance with the glimpses; the priority head is trained via a per-glimpse information-gain target from a lightweight memory bank, avoiding reinforcement learning. The approach achieves linear complexity in the number of glimpses and decouples peak memory from input resolution, consistently improving performance across nine fine-grained classification benchmarks.
The paper presents an end‑to‑end system that converts driving footage into dynamic vision sensor (DVS) event streams, augments training with simulated DVS data, and trains a convolutional spiking neural network (Conv‑SNN) to classify pedestrian crossing intent as crossing or non‑crossing. The Conv‑SNN, trained with a class‑balanced loss and surrogate‑gradient learning, achieves high accuracy and F1 scores on JAAD and CARLA DVS datasets, outperforming or matching prior frame‑based methods while operating on sparse temporal representations. The study details architectural choices, neuron dynamics, and training protocols, and provides a convergence analysis and domain‑transfer evaluation.