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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Published on: February 6, 2020

Chimera: a block-based neural architecture search framework for event-based object detection.

Diego A Silva¹, Ahmed Elsheikh², Kamilya Smagulova¹

¹Communication and Computing Systems Lab, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Frontiers in Artificial Intelligence

|October 17, 2025

Summary

This summary is machine-generated.

Chimera, a novel framework for event-based object detection, enhances processing of event camera data. This approach achieves state-of-the-art results with improved efficiency, outperforming existing methods.

Keywords:

Zero-Shot NAS event-based cameras hybrid neural networks neural architecture search neuromorphic datasets object detection

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Neuromorphic Engineering

Background:

Event-based cameras mimic the human eye, offering high-speed and low-power advantages.
Deep learning effectively processes event data, but optimization potential remains.
Adapting RGB-domain techniques to event data requires specialized frameworks.

Purpose of the Study:

Introduce Chimera, a Block-Based Neural Architecture Search (NAS) framework.
Systematically adapt RGB-domain processing methods for event-based object detection.
Explore macroblock combinations for optimized local and global processing.

Main Methods:

Developed Chimera, a NAS framework for event-based object detection.
Designed a search space using macroblocks: attention, convolutions, State Space Models, MLP-mixers.
Evaluated on the Prophesee GEN1 dataset.

Main Results:

Achieved state-of-the-art mean Average Precision (mAP) for event-based object detection.
Reduced model parameters by 1.6x compared to existing methods.
Demonstrated a 2.1x speed-up in processing.

Conclusions:

Chimera offers a systematic approach to optimizing deep learning for event-based vision.
The framework provides a flexible trade-off between processing capabilities and complexity.
Results indicate significant advancements in event-based object detection efficiency and performance.