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GazeMoE: Perception of Gaze Target with Mixture-of-Experts
arXiv – CS AI|Zhuangzhuang Dai, Zhongxi Lu, Vincent G. Zakka, Luis J. Manso, Jose M Alcaraz Calero, Chen Li|
🤖AI Summary
Researchers have developed GazeMoE, a new AI framework that uses Mixture-of-Experts architecture to accurately estimate where humans are looking by analyzing visual cues like eyes, head poses, and gestures. The system achieves state-of-the-art performance on benchmark datasets and addresses key challenges in gaze target detection through advanced multi-modal processing.
Key Takeaways
- →GazeMoE introduces a novel end-to-end framework for human gaze target estimation using Mixture-of-Experts architecture.
- →The system integrates multiple visual cues including eyes, head poses, gestures, and contextual features for improved accuracy.
- →The framework addresses class imbalance issues through auxiliary loss functions and strategic data augmentations.
- →GazeMoE achieves state-of-the-art performance on challenging gaze estimation benchmark datasets.
- →Code and pre-trained models have been released publicly on HuggingFace for research use.
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#computer-vision#mixture-of-experts#gaze-estimation#machine-learning#robotics#human-attention#ai-research#multi-modal
Read Original →via arXiv – CS AI
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