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<oembed><version>1.0</version><provider_name>Alignment for Vision-Language Foundation Models	</provider_name><provider_url>https://mscvprojects.ri.cmu.edu/2024team8</provider_url><title>About - Alignment for Vision-Language Foundation Models</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="j0iigPCbBY"&gt;&lt;a href="https://mscvprojects.ri.cmu.edu/2024team8/"&gt;About&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2024team8/embed/#?secret=j0iigPCbBY" width="600" height="338" title="&#x201C;About&#x201D; &#x2014; Alignment for Vision-Language Foundation Models	" data-secret="j0iigPCbBY" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><description>Text-to-visual models, which now create realistic images and videos, face challenges with complex prompts involving attributes, relationships, and advanced reasoning. Our study on GenAI-Bench evaluates top image and video generators for these tasks. We compare automated evaluation metrics against our collected human ratings and find that VQAScore significantly outperforms previous metrics such as CLIPScore. In &hellip; Continue reading ""</description><thumbnail_url>https://mscvprojectsricmu.files.wordpress.com/2024/05/1715380758753.png?w=1024</thumbnail_url></oembed>

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