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DTSTAMP:20251203T180430Z
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DTSTART;TZID=Europe/Copenhagen:20251211T100000
DTEND;TZID=Europe/Copenhagen:20251211T103000
DESCRIPTION:Foundation models (FMs) have emerged as a powerful paradigm\, e
 nabling a diverse range of data analytics and knowledge discovery tasks ac
 ross scientific fields. Inspired by the success of FMs—particularly larg
 e language models—researchers have recently begun to explore spatio-temp
 oral foundation models (STFMs) to improve adaptability and generalization 
 across a wide spectrum of spatio-temporal (ST) tasks. Despite rapid progre
 ss\, a systematic investigation of trajectory foundation models (TFMs)\, a
  crucial subclass of STFMs\, is largely lacking. This tutorial addresses t
 his gap by offering a comprehensive overview of recent advances in TFMs\, 
 including a taxonomy of existing methodologies and a critical analysis of 
 their strengths and limitations. In addition\, the tutorial highlights ope
 n challenges and outlines promising research directions to advance spatio-
 temporal general intelligence through the development of robust\, responsi
 ble\, and transferable TFMs.\n\nCo-sponsored by: Sean Bin Yang\n\nSpeaker(
 s): Sean Bin Yang\n\nRoom: 0.2.11\, Selma Lagerløfs Vej 300\, Aalborg Eas
 t\, Nordjyllands Amt\, Denmark\, 9220\, Virtual: https://events.vtools.iee
 e.org/m/519737
LOCATION:Room: 0.2.11\, Selma Lagerløfs Vej 300\, Aalborg East\, Nordjylla
 nds Amt\, Denmark\, 9220\, Virtual: https://events.vtools.ieee.org/m/51973
 7
ORGANIZER:seany@cs.aau.dk
SEQUENCE:24
SUMMARY: Spatio-Temporal Trajectory Foundation Model: Recent Advances and F
 uture Directions
URL;VALUE=URI:https://events.vtools.ieee.org/m/519737
X-ALT-DESC:Description: <br /><p class="MsoNormal" data-ogsb="white"><span 
 data-ogsc="black">Foundation models (FMs) have emerged as a powerful parad
 igm\, enabling a diverse range of data analytics and knowledge discovery t
 asks across scientific fields. Inspired by the success of FMs&mdash\;parti
 cularly large language models&mdash\;researchers have recently begun to ex
 plore spatio-temporal foundation models (STFMs) to improve adaptability an
 d generalization across a wide spectrum of spatio-temporal (ST) tasks. Des
 pite rapid progress\, a systematic investigation of trajectory foundation 
 models (TFMs)\, a crucial subclass of STFMs\, is largely lacking. This tut
 orial addresses this gap by offering a comprehensive overview of recent ad
 vances in TFMs\, including a taxonomy of existing methodologies and a crit
 ical analysis of their strengths and limitations. In addition\, the tutori
 al highlights open challenges and outlines promising research directions t
 o advance spatio-temporal general intelligence through the development of 
 robust\, responsible\, and transferable TFMs.&nbsp\;</span></p>\n<p class=
 "MsoNormal" data-ogsb="white"><span data-ogsc="black">&nbsp\;</span></p>
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