BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Advancing Multimodal Models Beyond Human Supervision X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2025-04-04-xudong-wang@cml.ics.uci.edu DTSTAMP:20250404T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20250404T140000 DTEND;TZID=America/Los_Angeles:20250404T150000 SUMMARY:[CML Seminar] XuDong Wang: Advancing Multimodal Models Beyond Human Supervision LOCATION:Donald Bren Hall 4011 DESCRIPTION:XuDong Wang\, PhD Student\, Berkeley AI Research Lab\, Universi ty of California\, Berkeley\n\nTitle: Advancing Multimodal Models Beyond H uman Supervision\n\nAbstract: To advance AI toward true artificial general intelligence\, it is crucial to incorporate a wider range of sensory inpu ts\, including physical interaction\, spatial navigation\, and social dyna mics. However\, achieving the successes of self-supervised Large Language Models (LLMs) across other modalities in our physical and digital environm ents remains a significant challenge. In this talk\, I will discuss how se lf-supervised learning methods can be harnessed to advance multimodal mode ls beyond the need for human supervision. Firstly\, I will highlight a ser ies of research efforts on self-supervised visual scene understanding that leverage the capabilities of self-supervised models to segment anything w ithout the need for 1.1 billion labeled segmentation masks. Secondly\, I w ill demonstrate how generative and understanding models can work together synergistically. Lastly\, I will explore the increasingly important techni ques for learning from unlabeled or imperfect data within the context of d ata-centric representation learning.\n\nhttps://cml.ics.uci.edu/seminars/2 025-04-04-xudong-wang X-ALT-DESC;FMTTYPE=text/html:XuDong Wang\, PhD Student\, Berkeley AI Research Lab\, University of California\, Berkeley

Title: Advancing Multimodal Models Beyond Human Supervision

Abstract: To advance AI toward true artificial general intelligence\, it is crucial to incorporate a wider range of sensory inputs\, including p hysical interaction\, spatial navigation\, and social dynamics. However\, achieving the successes of self-supervised Large Language Models (LLMs) ac ross other modalities in our physical and digital environments remains a s ignificant challenge. In this talk\, I will discuss how self-supervised le arning methods can be harnessed to advance multimodal models beyond the ne ed for human supervision. Firstly\, I will highlight a series of research efforts on self-supervised visual scene understanding that leverage the ca pabilities of self-supervised models to segment anything without the need for 1.1 billion labeled segmentation masks. Secondly\, I will demonstrate how generative and understanding models can work together synergistically. Lastly\, I will explore the increasingly important techniques for learnin g from unlabeled or imperfect data within the context of data-centric repr esentation learning.

https://cml.ics.uci.edu/seminars/2025-04-04-xudong-wan g URL:https://cml.ics.uci.edu/seminars/2025-04-04-xudong-wang END:VEVENT END:VCALENDAR