Detecting Context Shifts in the Human Experience Using Multimodal Foundation Models
Detecting context shifts in human experience is critical for applications in cognitive modeling, human-AI interaction, and adaptive neurotechnology. However, formalizing and identifying these shifts in real-world settings remains challenging due to annotation inconsistencies, data sparsity, and the multimodal nature of human perception.
Authors
- Cory S. Inman (ORCID: https://orcid.org/0000-0003-2928-6252)
- Marina Kogan (ORCID: https://orcid.org/0000-0002-9200-6914)
- Luis Antonio Ribot García (ORCID: https://orcid.org/0000-0002-5111-0694)
- Mani Srivastava (ORCID: https://orcid.org/0000-0002-3782-9192)
- Alireza Kazemi (ORCID: https://orcid.org/0000-0001-8944-1459)
- Liying Han (ORCID: https://orcid.org/0000-0001-9748-0597)
- Iris Nguyen
- Burke Dambly
Institutions
- Amazon (United States) (US)
- University of California, Los Angeles (US)
- University of Utah (US)
Publication Details
- Published
- 2025-05-04
- DOI
- https://doi.org/10.1145/3715014.3724037
- Citations
- 1
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.28
Funders
- National Science Foundation
- National Institutes of Health