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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2301.10931/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2301.10931/feed.xml" rel="self" type="application/rss+xml" /><item><title>Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning — mentioned by AndrewKemendo</title><link>https://news.ycombinator.com/item?id=36801370</link><guid isPermaLink="false">urn:hackerpapers:mention:2301.10931:36801370</guid><pubDate>Thu, 20 Jul 2023 14:53:45 GMT</pubDate><description>&lt;p&gt;+1 I’ll “yes and” here…beyond AR/VR a more powerful use case is multi-modal learning (with RL) which is what Meta is probably the leader in IMO. Example paper here: “ Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning” https://arxiv.org/abs/2301.10931 This IMO is the pathway to AGI, as it combines all sense-plan-do data into a time coordinated stream and mimics how humans transfer learning to children via demonstration recording and behavior authoring. If we can create robotics with locomotion and dexterous manipulation…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2301.10931/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
