The paper presents a lightweight, training‑free framework for real‑time unsupervised object discovery from asynchronous event camera streams. It introduces a linear‑time Spatio‑Temporal Probabilistic Event Filter (SPEF) that adaptively distinguishes salient motion from noise, and an Event Morton Code Clustering (EMCC) module that efficiently groups events without costly distance calculations. Experiments on E‑MLB, FRED, and eTraM datasets show SPEF outperforms classical filters and competes with learning‑based methods, while EMCC achieves the highest accuracy and fastest execution among density‑based clustering baselines.
arXiv:2606. 12826v1 Announce Type: cross Abstract: Moving instance segmentation (MIS) attracts increasing attention due to its broad applications in traffic surveillance, autonomous driving, and animal tracking.
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...
The paper introduces OccLinker, a lightweight plugin for vision‑based occupancy networks that reduces flickering by efficiently merging historical static and motion cues with current features via a dual cross‑attention mechanism. It generates correction components to refine base network predictions and proposes a new temporal consistency metric to quantify flickering. Experiments on two benchmark datasets show that OccLinker improves performance with minimal computational overhead while effectively diminishing flickering artifacts.
arXiv:2608.31023v1 Announce Type: new Abstract: We study dynamic Gaussian Splatting from monocular videos. While recent advancements in dynamic Gaussian splatting offer a promising foundation for mod...
PointEvent introduces serialized motion evidence accumulation for event-based tiny object detection, treating motion continuity as an ordered evidence propagation process. The method organizes event streams into locality‑preserving spatiotemporal paths and chronology‑preserving temporal paths, alternating serialized scans across complementary orders to consolidate fragmented motion evidence. A lightweight event‑wise state‑space framework with a high‑resolution event branch and compact context modulation achieves state‑of‑the‑art performance with the fewest parameters and fastest inference among compared methods.
arXiv:2606. 14094v1 Announce Type: cross Abstract: Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information.
arXiv:2610.20501v1 Announce Type: new Abstract: Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data. Existi...
arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised...
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.
We study dynamic Gaussian Splatting from monocular videos. While recent advancements in dynamic Gaussian splatting offer a promising foundation for modeling dynamic scenes, they often overfit to the t...
arXiv:2609.22500v1 Announce Type: new Abstract: Autonomous navigation requires precise and efficient semantic segmentation, yet existing frame-based approaches remain limited by motion blur, glare, l...
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.