MAKING DIALOGUE GROUNDING DATA RICH: A THREE-TIER DATA SYNTHESIS FRAMEWORK FOR GENERALIZED REFERRING EXPRESSION COMPREHENSION
Juexi Shao, Yujian Gan, Christopher Madge, Vanja Karan, Massimo Poesio
Abstract
Dialogue-Based Generalized Referring Expression Comprehension (GREC) requires models to ground the expression and unlimited targets in complex visual scenes while resolving coreference across a long dialogue context. However, existing systems struggle under distribution shift between training and evaluation domains, a gap exacerbated by the scarcity of annotated dialogue grounding data. We address this challenge with a three-tier data-synthesis method that balances realism and controllability to produce scalable supervision for dialogue-conditioned grounding. Fine-tuning on the synthesized data yields consistent, substantial improvements over prior approaches across standard evaluation metrics.
BibTeX
@inproceedings{icassp2026_makingdialoguegr,
title = {MAKING DIALOGUE GROUNDING DATA RICH: A THREE-TIER DATA SYNTHESIS FRAMEWORK FOR GENERALIZED REFERRING EXPRESSION COMPREHENSION},
author = {Juexi Shao and Yujian Gan and Christopher Madge and Vanja Karan and Massimo Poesio},
booktitle = {ICASSP 2026},
year = {2026}
}