From Data Scaling to Data Intelligence: Data-Centric Learning for Generalizable Multimodal Foundation Models

PhD Qualifying Examination


Title: "From Data Scaling to Data Intelligence: Data-Centric Learning for 
Generalizable Multimodal Foundation Models"

by

Mr. Haokun GUI


Abstract:

Multimodal foundation models increasingly depend on heterogeneous data 
spanning vision, language, audio, embodied trajectories, and model-generated 
interactions. As these systems expand beyond web-scale supervision, data 
become costly, unevenly distributed, and difficult to scale, making data 
utility as important as data volume.

This survey develops a data-centric framework for multimodal foundation models 
across the data lifecycle. It organizes existing approaches around data 
construction, cleaning and alignment, representation learning, adaptive 
information selection, and feedback-driven improvement. The survey examines 
how these mechanisms improve coverage, transferability, sample efficiency, and 
generalization under limited data and compute budgets. Emerging evidence 
suggests a shift from data scaling toward data intelligence, where models 
increasingly optimize which data to collect, how to structure and exploit 
them, and how to convert inference-time interactions and failures into new 
supervision. Closed-loop data generation, valuation, and reuse remain central 
research challenges.


Date:                   Monday, 24 August 2026

Time:                   11:00am - 1:00pm

Venue:                  Room 2408
                        Lift 17/18

Committee Members:      Prof. Jiaya Jia (Supervisor)
                        Dr. Dan Xu (Chairperson)
                        Dr. May Fung