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		<title>New AI Reads Eyes With Uncertainty Built In, Promising Safer Driver Monitoring</title>
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		<pubDate>Mon, 05 Oct 2026 02:32:31 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Attention Mechanisms]]></category>
		<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[driver monitoring]]></category>
		<category><![CDATA[EyeDiap]]></category>
		<category><![CDATA[gaze estimation]]></category>
		<category><![CDATA[GazeCapture]]></category>
		<category><![CDATA[Human-computer interaction]]></category>
		<category><![CDATA[MPIIFaceGaze]]></category>
		<category><![CDATA[multimodal fusion]]></category>
		<category><![CDATA[real-time inference]]></category>
		<category><![CDATA[uncertainty quantification]]></category>
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					<description><![CDATA[Researchers have developed AttentiveGaze, a real-time multimodal AI framework that fuses face, eye, and head pose cues with attention mechanisms and predicts a confidence value for every gaze estimate, improving robustness in real-world conditions and enabling safer applications such as driver monitoring and clinical assessment.]]></description>
		
		
		
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