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

• URL: http://arxiv.org/abs/2606.22378v1
• Date: Sun, 21 Jun 2026 07:56:34 GMT
• Title: Following the Flow: Advection-Consistent Modeling for Event-based Small Object Detection
• Abstract summary: Event cameras enable high-frequency visual perception with microsecond latency, offering advantages for dynamic scenes.<n>However, event-based small object detection remains challenging due to sparse asynchronous measurements and weak object responses that are easily disrupted by noise.<n>We propose a physics-guided advection-consistent framework, termed PACT, which formulates event evolution as a motion-driven feature transport process.
• Score: 2.925778409623925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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