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		<title>Memory-Guided AI Learns What Normal Looks Like to Spot Surveillance Anomalies</title>
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		<pubDate>Mon, 05 Oct 2026 18:43:39 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[future-frame prediction]]></category>
		<category><![CDATA[memory banks]]></category>
		<category><![CDATA[recurrent neural networks]]></category>
		<category><![CDATA[ShanghaiTech]]></category>
		<category><![CDATA[spatial feature enhancement]]></category>
		<category><![CDATA[surveillance video]]></category>
		<category><![CDATA[temporal attention]]></category>
		<category><![CDATA[UCSD Ped2]]></category>
		<category><![CDATA[unsupervised learning]]></category>
		<category><![CDATA[video anomaly detection]]></category>
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					<description><![CDATA[A new prediction-based neural network called RMTA-Net combines recurrent temporal processing, learnable memory banks, and adaptive attention to detect video anomalies without labeled data, achieving strong results on three surveillance benchmarks.]]></description>
		
		
		
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