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	<title>autoencoder &#8211; BIOENGINEER.ORG</title>
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		<title>Hybrid CNN-Transformer AI Spots Anomalies in Surveillance Video With Record Accuracy</title>
		<link>https://bioengineer.org/hybrid-cnn-transformer-ai-spots-anomalies-in-surveillance-video-with-record-accuracy/</link>
		
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		<pubDate>Tue, 06 Oct 2026 01:39:00 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[autoencoder]]></category>
		<category><![CDATA[CNN]]></category>
		<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Farneback]]></category>
		<category><![CDATA[memory module]]></category>
		<category><![CDATA[optical flow]]></category>
		<category><![CDATA[surveillance]]></category>
		<category><![CDATA[UCSD Ped2]]></category>
		<category><![CDATA[unsupervised learning]]></category>
		<category><![CDATA[video anomaly detection]]></category>
		<category><![CDATA[vision transformer]]></category>
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					<description><![CDATA[Researchers have developed a memory-augmented CNN-vision transformer autoencoder that uses Farneback optical flow to detect anomalies in surveillance video with up to 98.97 percent AUC on standard benchmarks.]]></description>
		
		
		
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