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Survey maps dynamic neural networks for computer vision and sensor fusion

By PulseAugur Editorial · [1 sources] ·

This survey paper provides a comprehensive overview of Dynamic Neural Networks (DNNs), focusing on their application in computer vision and multi-modal sensor fusion. It addresses the challenge of deploying large models on embedded devices by explaining how DNNs adapt computation levels based on input complexity, unlike static optimization methods. The paper categorizes DNNs by their adaptive components—output, computation graph, or input—and highlights their potential benefits for sensor fusion tasks, such as improved adaptivity and noise reduction. AI

IMPACT Provides a unified taxonomy and repository for dynamic neural network research, potentially accelerating development in adaptive AI systems for resource-constrained environments.

RANK_REASON This is a survey paper published on arXiv detailing research in Dynamic Neural Networks.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey maps dynamic neural networks for computer vision and sensor fusion

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This is a survey paper published on arXiv detailing research in Dynamic Neural Networks.
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160 days old
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Fabio Montello, Ronja G\"uldenring, Simone Scardapane, Lazaros Nalpantidis ·

    A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion

    arXiv:2501.07451v4 Announce Type: replace Abstract: Model compression is essential in the deployment of large Computer Vision models on embedded devices. However, static optimization techniques (e.g. pruning, quantization, etc.) neglect the fact that different inputs have differe…

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