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New GazeLNN model predicts human attention for robot navigation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed GazeLNN, a novel and computationally efficient model for predicting human visual attention in real-time. This model utilizes Liquid Neural Networks and MobileNetV3 to predict fixation heatmaps with significantly reduced computational costs and accelerated inference times compared to existing methods. GazeLNN has been integrated into an autonomous navigation system for aerial robots, demonstrating its practical application in guiding robot perception through human-fixation-based active perception. AI

IMPACT This research could lead to more efficient and human-like visual perception systems in autonomous robots.

RANK_REASON The cluster contains an academic paper detailing a new model and its application.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New GazeLNN model predicts human attention for robot navigation

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The cluster contains an academic paper detailing a new model and its application.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Fatma Youssef Mohammed, Grzegorz Malczyk, Kostas Alexis ·

    Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation

    arXiv:2606.20491v1 Announce Type: cross Abstract: Human visual attention relies on structured scanpaths to efficiently process scenes, yet instilling this behavior into robot autonomy is in its infancy and hindered by the high,computational costs of existing predictive models. To…

  2. arXiv cs.CV TIER_1 English(EN) · Kostas Alexis ·

    Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation

    Human visual attention relies on structured scanpaths to efficiently process scenes, yet instilling this behavior into robot autonomy is in its infancy and hindered by the high,computational costs of existing predictive models. To address this, we introduce GazeLNN, a computation…

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