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

Event cameras enable high-frequency visual perception withmicrosecond latency, offering advantages for dynamic scenes. However,event-based small object detection remains challenging due to sparseasynchronous measurements and weak object responses that are easilydisrupted by noise. Limited spatial support causes small-object signalsto lose temporal continuity, resulting in fragmented and unstable pre-dictions. To address this issue, we propose a physics-guided advection-consistent modeling framework, termed PACT, which formulates eventevolution as a motion-driven feature transport process. Instead of rely-ing solely on local spatio-temporal aggregation, PACT propagates fea-tures along estimated velocity fields and enforces trajectory-level consis-tency through advection constraints. This design preserves weak eventresponses over time and prevents their degradation under complex back-ground interference. Technically, PACT integrates motion-aware featureextraction with a differentiable advection-based transport operator, en-abling coherent motion representation and effective noise suppressionduring 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. Thecode will be made publicly available.