Following the Flow: Advection-Consistent Modeling for Event-based Small Object Detection

2026-06-21

AI Summary

This work addresses the challenges of small object detection with event cameras, where sparse, asynchronous signals and weak responses are highly susceptible to noise, leading to temporal discontinuity and unstable predictions. To overcome these limitations, the authors propose PACT, a physics-guided convection consistency modeling framework that, for the first time, introduces a convection conservation mechanism to model event evolution as a motion-driven feature transport process. Specifically, features are propagated along an estimated velocity field using a differentiable convection operator, enabling motion-aware feature extraction and noise suppression, while trajectory-level consistency constraints preserve temporal continuity of weak responses. Experiments demonstrate that PACT achieves a 20.72% improvement in IoU and a 15.03% gain in accuracy on standard event-based datasets, with computational efficiency comparable to existing methods.

Abstract

Event cameras enable high-frequency visual perception with microsecond latency, offering advantages for dynamic scenes. However, event-based small object detection remains challenging due to sparse asynchronous measurements and weak object responses that are easily disrupted by noise. Limited spatial support causes small-object signals to lose temporal continuity, resulting in fragmented and unstable predictions. To address this issue, we propose a physics-guided advection-consistent modeling framework, termed PACT, which formulates event evolution as a motion-driven feature transport process. Instead of relying solely on local spatio-temporal aggregation, PACT propagates features along estimated velocity fields and enforces trajectory-level consistency through advection constraints. This design preserves weak event responses over time and prevents their degradation under complex background interference. Technically, PACT integrates motion-aware feature extraction with a differentiable advection-based transport operator, enabling coherent motion representation and effective noise suppression during temporal evolution. Extensive experiments on benchmark event-based datasets demonstrate that PACT consistently outperforms state-of-the-art methods, achieving improvements of 20.72\% in IoU and 15.03\% in accuracy while maintaining comparable computational efficiency. The code is publicly available at https://github.com/fulongcai/PACT.

Problem

• event-based vision
• small object detection
• temporal continuity
• sparse asynchronous events
• noise interference

Innovation

• advection-consistent modeling
• event-based vision
• small object detection
• motion-aware feature transport
• trajectory-level consistency