# Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection

Source: [Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection](<https://www.sota2.com/research/paper/68fa76711e209131cea6d059>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:45:57.319Z

Authors: Diego A. Silva, Ahmed Elsheikh, Kamilya Smagulova, Mohammed E. Fouda, Ahmed M. Eltawil

Published: 2024-12-27T13:50:44.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.19646>)

[PDF](<https://arxiv.org/pdf/2412.19646.pdf>)