2 citations · 2 across the 8 of their papers we have counts for
8 papers
Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning
Enhan Zhao, Wei Wu, Yuanrui Zhang +2
Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs). Existing approaches largely rely on large-scale, statically curated datasets, where a…
From Foundation to Application: Improving VLA Models in Practice
Wei Wu, Fangjing Wang, Fan Lu +21
Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bri…
A Pragmatic VLA Foundation Model
Wei Wu, Fan Lu, Yunnan Wang +22
Offering great potential in robotic manipulation, a capable Vision-Language-Action (VLA) foundation model is expected to faithfully generalize across tasks and platforms while ensu…
Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning
Fan Lu, Wei Wu, Kecheng Zheng +7
Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have dev…
Learning Visual Generative Priors without Text
Shuailei Ma, Kecheng Zheng, Ying Wei +7
Although text-to-image (T2I) models have recently thrived as visual generative priors, their reliance on high-quality text-image pairs makes scaling up expensive. We argue that gra…
LoTLIP: Improving Language-Image Pre-training for Long Text Understanding
Wei Wu, Kecheng Zheng, Shuailei Ma +7
Understanding long text is of great demands in practice but beyond the reach of most language-image pre-training (LIP) models. In this work, we empirically confirm that the key rea…
DreamLIP: Language-Image Pre-training with Long Captions
Kecheng Zheng, Yifei Zhang, Wei Wu +5
Language-image pre-training largely relies on how precisely and thoroughly a text describes its paired image. In practice, however, the contents of an image can be so rich that wel…
TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
Qinying Liu, Wei Wu, Kecheng Zheng +6
The crux of learning vision-language models is to extract semantically aligned information from visual and linguistic data. Existing attempts usually face the problem of coarse ali…