让事件相机在200赫兹下实现高精度目标检测，同时保持低延迟。
FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection
- 用柱状编码保留事件时间细节，避免传统分帧的信息丢失。
- 在100~200赫兹下检测性能超越现有方法，参数和推理开销几乎不变。
- 适合高速动态场景下的实时视觉任务，如机器人导航、自动驾驶。
事件相机是仿生传感器，能异步捕捉对数强度变化，在高速与高动态范围场景中具有天然优势。然而，事件流稀疏且异步的特性给现代深度学习架构带来根本挑战。现有方法通常将积累窗口划分为固定的时间子区间，虽利于空间处理，但丢弃了精细的时间结构，并限制推理频率受制于训练标注的低频。为此，本文提出FATE统一框架，核心为新颖的柱状编码（Pillar Encoding, PE）。在宏观积累窗口内，PE不进行内部时间分块，而是将事件按空间柱组织，并通过连续时间正交多项式基投影近似窗内演化过程。该方法生成最优L2表示，以密集伪图像形式保留丰富时间动态，缓解稀疏事件下的信息损失。为进一步利用该表示，提出频率感知训练（FAT），一种软均值教师课程机制，生成时序稠密伪标签，有效弥合低频监督与高频推理之间的差异。大量实验表明，FATE可泛化至多种网络架构，持续优于强基线。其能在高达200赫兹的高时间分辨率下实现鲁棒目标检测，同时参数量与推理延迟增加极少。
原文摘要 · Abstract (English)
Event cameras are bio-inspired sensors that asynchronously capture logarithmic intensity changes, offering inherent advantages in high-speed and high-dynamic-range scenarios. However, the sparse and asynchronous nature of event streams poses a fundamental challenge for modern deep learning architectures. To enable compatibility with standard models, most existing approaches partition the accumulation window into fixed temporal sub-bins. While effective for spatial processing, this internal discretization discards fine-grained temporal structure and constrains inference to the low temporal frequencies imposed by training supervision. To address this limitation, we propose FATE, a unified framework built upon a novel Pillar Encoding (PE). While operating over discrete macro-accumulation windows dictated by the target frequency, PE avoids internal temporal sub-binning. It organizes events into spatial pillars and approximates their intra-window evolution via projection onto a continuous-time orthogonal polynomial basis. This formulation yields an L2-optimal representation that retains rich temporal dynamics in a dense pseudo-image, mitigating information loss under sparse event conditions. To fully leverage this representation, we introduce Frequency-Aware Training (FAT), a soft mean-teacher curriculum that generates temporally dense pseudo-labels, effectively bridging the mismatch between low-frequency supervision and high-frequency inference. Extensive experiments demonstrate that FATE generalizes across architectural paradigms and consistently outperforms strong baselines. It enables robust object detection at high temporal resolutions up to 200 Hz, while incurring minimal overhead in parameter count and inference latency
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