2 citations · 2 across the 5 of their papers we have counts for
5 papers
On the Difficulty of Learning a Meta-network for Training Data Selection
Zilin Du, Junqi Zhao, Boyang Albert Li
Synthetic data are increasingly used to train neural networks, yet distributional mismatch with real data limits their effectiveness when used indiscriminately. A common strategy i…
Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding
Zilin Du, Haoxin Li, Jianfei Yu +1
Visual grounding aims to localize the image regions based on a textual query. Given the difficulty of large-scale data curation, we investigate how to effectively learn visual grou…
Generating Synthetic Datasets for Few-shot Prompt Tuning
Xu Guo, Zilin Du, Boyang Li +1
A major limitation of prompt tuning is its dependence on large labeled training datasets. Under few-shot learning settings, prompt tuning lags far behind full-model fine-tuning, li…
Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQA
Miaoyu Li, Haoxin Li, Zilin Du +1
Knowledge-based Visual Question-answering (K-VQA) often requires the use of background knowledge beyond the image. However, we discover that a single knowledge generation strategy…
Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction
Zilin Du, Haoxin Li, Xu Guo +1
The task of multimodal relation extraction has attracted significant research attention, but progress is constrained by the scarcity of available training data. One natural thought…