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	<title>fine-grained recognition &#8211; BIOENGINEER.ORG</title>
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		<title>Hypergraph Adapters Push Parameter-Efficient Multimodal Fine-Tuning to New Heights</title>
		<link>https://bioengineer.org/hypergraph-adapters-push-parameter-efficient-multimodal-fine-tuning-to-new-heights/</link>
		
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		<pubDate>Mon, 05 Oct 2026 13:53:49 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[adapter modules]]></category>
		<category><![CDATA[CLIP]]></category>
		<category><![CDATA[few-shot learning]]></category>
		<category><![CDATA[fine-grained recognition]]></category>
		<category><![CDATA[hypergraph adapters]]></category>
		<category><![CDATA[Hypergraph Neural Networks]]></category>
		<category><![CDATA[inductive bias]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[message passing]]></category>
		<category><![CDATA[multimodal learning]]></category>
		<category><![CDATA[parameter-efficient fine-tuning]]></category>
		<category><![CDATA[vision-language models]]></category>
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					<description><![CDATA[Researchers have introduced HGA-Net, a hypergraph-based adapter that connects related examples within each mini-batch and achieves near-perfect fine-grained recognition with only 1.573 million trainable parameters on a frozen CLIP backbone.]]></description>
		
		
		
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