2 citations · 3 across the 6 of their papers we have counts for
6 papers
Enhancing Vision-Language Compositional Understanding with Multimodal Synthetic Data
Haoxin Li, Boyang Li
Paired image-text data with subtle variations in-between (e.g., people holding surfboards vs. people holding shovels) hold the promise of producing Vision-Language Models with prop…
Learning to Animate Images from A Few Videos to Portray Delicate Human Actions
Haoxin Li, Yingchen Yu, Qilong Wu +3
Despite recent progress, video generative models still struggle to animate static images into videos that portray delicate human actions, particularly when handling uncommon or nov…
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…
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…
Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground
Haoxin Li, Yuan Liu, Hanwang Zhang +1
In video action recognition, shortcut static features can interfere with the learning of motion features, resulting in poor out-of-distribution (OOD) generalization. The video back…