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 computationally lightweight,scanpath prediction model that leverages Liquid Neural Networks as its recurrent engine and employs MobileNetV3 for feature extraction. Operating auto-regressively, the architecture predicts sequential fixation heatmaps conditioned on the current visual stimulus and fixation history. Despite requiring only 0.61 GFLOPs, GazeLNN achieves state-of-the-art performance on the MIT Low Resolution dataset...
| Authors | Fatma Youssef Mohammed, Grzegorz Malczyk, Kostas Alexis |
|---|---|
| Primary category | cs.RO |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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