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<oembed><version>1.0</version><provider_name>Learning diffusion priors for video amodal segmentation	</provider_name><provider_url>https://mscvprojects.ri.cmu.edu/2024team7</provider_url><title>Project Summary - Learning diffusion priors for video amodal segmentation</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="7pHRAebps5"&gt;&lt;a href="https://mscvprojects.ri.cmu.edu/2024team7/"&gt;Project Summary&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2024team7/embed/#?secret=7pHRAebps5" width="600" height="338" title="&#x201C;Project Summary&#x201D; &#x2014; Learning diffusion priors for video amodal segmentation	" data-secret="7pHRAebps5" 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>Abstract Object permanence in humans is a fundamental cue that helps in understanding persistence of objects, even when they are fully occluded in the scene. Present day methods in object segmentation do not account for this amodal nature of the world, and only work for segmentation of visible or modal objects. Few amodal methods exist; &hellip; Continue reading ""</description><thumbnail_url>https://mscvprojects.ri.cmu.edu/2024team7/wp-content/uploads/sites/105/2024/12/Figure-teaser-1024x417.png</thumbnail_url></oembed>

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