Table of Contents: :: Library Catalog

Saved in:

Bibliographic Details
Main Authors:	Chen, Zhe, Wang, Weiyun, Tian, Hao, Ye, Shenglong, Gao, Zhangwei, Cui, Erfei, Tong, Wenwen, Hu, Kongzhi, Luo, Jiapeng, Ma, Zheng, Ma, Ji, Wang, Jiaqi, Dong, Xiaoyi, Yan, Hang, Guo, Hewei, He, Conghui, Shi, Botian, Jin, Zhenjiang, Xu, Chao, Wang, Bin, Wei, Xingjian, Li, Wei, Zhang, Wenjian, Zhang, Bo, Cai, Pinlong, Wen, Licheng, Yan, Xiangchao, Dou, Min, Lu, Lewei, Zhu, Xizhou, Lu, Tong, Lin, Dahua, Qiao, Yu, Dai, Jifeng, Wang, Wenhai
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2404.16821
Tags:	Add Tag No Tags, Be the first to tag this record!

Table of Contents:

In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements: (1) Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation model -- InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. (2) Dynamic High-Resolution: we divide images into tiles ranging from 1 to 40 of 448$\times$448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input. (3) High-Quality Bilingual Dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks. Code has been released at https://github.com/OpenGVLab/InternVL.

Similar Items