POINTS1.5: Building a Vision-Language Model towards Real World
  Applications
Authors: name: Yuan Liu, name: Le Tian, name: Xiao Zhou, name: Xinyu Gao, name: Kavio Yu, name: Yang Yu, name: Jie Zhou

Vision-language models have made significant strides recently, demonstrating
superior performance across a range of tasks, e.g. optical character
recognition and complex diagram analysis. Building on this trend, we introduce
a new vision-language model, POINTS1.5, designed to excel in various real-world
applications. POINTS1.5 is an enhancement of POINTS1.0 and incorporates several
key innovations: i) We replace the original CLIP vision encoder, which had a
fixed image resolution, with a NaViT-style vision encoder that supports native
dynamic high resolution. This allows POINTS1.5 to process images of any
resolution without needing to split them into tiles. ii) We add bilingual
support to POINTS1.5, significantly enhancing its capability in Chinese. Due to
the scarcity of open-source Chinese datasets for vision-language models, we
collect numerous images from the Internet and annotate them using a combination
of manual and automatic methods. iii) We propose a set of rigorous filtering
methods for visual instruction tuning datasets. We comprehensively evaluate all
these filtering methods, and choose the most effective ones to obtain the final
visual instruction tuning set. Thanks to these innovations, POINTS1.5
significantly outperforms POINTS1.0 and demonstrates strong performance across
a range of real-world applications. Notably, POINTS1.5-7B is trained on fewer
than 4 billion tokens and ranks first on the OpenCompass leaderboard among
models with fewer than 10 billion parameters