跟随流动：面向事件相机小目标检测的平流一致性建模
Following the Flow: Advection-Consistent Modeling for Event-based Small Object Detection
AI总结：
针对事件相机小目标检测中稀疏异步测量和噪声干扰导致预测碎片化的问题，提出物理引导的平流一致性建模框架PACT，通过沿估计速度场传播特征并施加轨迹级一致性约束，保留弱事件响应，在基准数据集上IoU提升20.72%，准确率提升15.03%。
摘要：
事件相机能够以微秒级延迟实现高频视觉感知，在动态场景中具有优势。然而，由于稀疏的异步测量和易受噪声干扰的弱目标响应，基于事件的小目标检测仍然具有挑战性。有限的空间支持导致小目标信号失去时间连续性，产生碎片化和不稳定的预测。为解决这一问题，我们提出了一种物理引导的平流一致性建模框架，称为PACT，该框架将事件演化表述为运动驱动的特征传输过程。PACT不依赖于局部时空聚合，而是沿估计的速度场传播特征，并通过平流约束强制执行轨迹级一致性。这种设计能够随时间保留弱事件响应，并防止其在复杂背景干扰下退化。在技术上，PACT将运动感知特征提取与可微分的基于平流的传输算子相结合，从而在时间演化过程中实现连贯的运动表示和有效的噪声抑制。在基准事件数据集上的大量实验表明，PACT持续优于最先进的方法，在保持相当计算效率的同时，IoU提升了20.72%，准确率提升了15.03%。代码已公开在https://this URL。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
