构建三阶段数据合成框架，提升对话指代理解的泛化能力
Making Dialogue Grounding Data Rich: A Three-Tier Data Synthesis Framework for Generalized Referring Expression Comprehension
- 分三阶段合成逼真且可控的对话指代表达数据
- 在多个标准指标上显著优于现有方法
- 适合需要强泛化性的视觉对话与指代理解研究者
基于对话的广义指代理解（GREC）要求模型在复杂视觉场景中定位表达内容，并在长对话上下文中解决指代消解问题。然而，现有系统在训练与评估域分布差异下表现不佳，主要因标注对话接地数据稀缺。本文提出一种三阶段数据合成方法，在真实性和可控性间取得平衡，生成可用于对话条件接地任务的大规模监督数据。在合成数据上微调模型后，各项标准评估指标均实现一致且显著提升。
原文摘要 · Abstract (English)
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.
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