为事件相机设计的神经架构搜索框架，提升目标检测效率。
Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection
- 基于模块化块构建搜索空间，融合注意力与状态空间模型
- 在PEDRo数据集上性能媲美顶尖模型，参数量减少1.6倍
- 适合事件感知系统研发者快速定制轻量级检测模型
事件相机模拟人类视觉，具备高速鲁棒性和低功耗优势。现有深度学习方法在处理事件数据方面已展现有效性。Chimera 是一种专为事件域目标检测设计的基于块的神经架构搜索（NAS）框架，旨在系统性地将适用于RGB域的方法迁移到事件域。其设计空间由多种宏块构成，包括注意力模块、卷积、状态空间模型及MLP-mixer结构，兼顾局部与全局处理能力，并提供不同复杂度的权衡。在PErson Detection in Robotics（PEDRo）数据集上的实验表明，Chimera的性能可与当前最先进模型相媲美，同时平均参数量减少1.6倍。
原文摘要 · Abstract (English)
Event-based cameras are sensors that simulate the human eye, offering advantages such as high-speed robustness and low power consumption. Established Deep Learning techniques have shown effectiveness in processing event data. Chimera is a Block-Based Neural Architecture Search (NAS) framework specifically designed for Event-Based Object Detection, aiming to create a systematic approach for adapting RGB-domain processing methods to the event domain. The Chimera design space is constructed from various macroblocks, including Attention blocks, Convolutions, State Space Models, and MLP-mixer-based architectures, which provide a valuable trade-off between local and global processing capabilities, as well as varying levels of complexity. The results on the PErson Detection in Robotics (PEDRo) dataset demonstrated performance levels comparable to leading state-of-the-art models, alongside an average parameter reduction of 1.6 times.
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