April 10, 2024, 4:45 a.m. | Zhenhailong Wang, Joy Hsu, Xingyao Wang, Kuan-Hao Huang, Manling Li, Jiajun Wu, Heng Ji

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.06479v1 Announce Type: cross
Abstract: While large multimodal models excel in broad vision-language benchmarks, they often struggle with tasks requiring precise perception of low-level visual details, such as comparing line lengths or solving simple mazes. In particular, this failure mode persists in question-answering tasks about vector graphics -- images composed purely of 2D objects and shapes. To address this challenge, we propose the Visually Descriptive Language Model (VDLM), which performs text-based reasoning about vector graphics. VDLM leverages Scalable Vector Graphics …

arxiv cs.ai cs.cl cs.cv graphics reasoning text type vector

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Risk Management - Machine Learning and Model Delivery Services, Product Associate - Senior Associate-

@ JPMorgan Chase & Co. | Wilmington, DE, United States

Senior ML Engineer (Speech/ASR)

@ ObserveAI | Bengaluru