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  <title><![CDATA[Ph.D. Proposal Oral Exam - Apoorva Beedu]]></title>
  <body><![CDATA[<p><strong>Title:&nbsp; </strong><em>Learning Joint Action and Object Embedding for the Task of Video Summarization in Cooking Videos</em></p>

<p><strong>Committee:&nbsp; </strong></p>

<p>Dr. Essa, Advisor</p>

<p>Dr. Romberg, Co-Advisor&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>

<p>Dr. Heck, Chair</p>

<p>Dr. Ploetz</p>

<p><strong>Abstract: </strong>The objective of the proposed research is to model a joint action and object embedding to infer the object&rsquo;s current state based on the action that has already occurred, or alternatively, to recognise the action that had to take place for the object to arrive at it&rsquo;s current state. To reason about an object&#39;s state, it helps to know what prior actions took place for the object to arrive at it&#39;s current state. Additionally, given an object, there is a set of plausible actions that can act upon the object. With this motivation, we propose to model a joint object-action embedding, that is cognizant of the interdependencies between the action and object&#39;s state, and propose to&nbsp; evaluate this knowledge on tasks like action anticipation and video summarization in cooking videos.</p>
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