VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis

Bibliographic Details
Title: VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
Authors: Pang, Chao, Weng, Xingxing, Wu, Jiang, Li, Jiayu, Liu, Yi, Sun, Jiaxing, Li, Weijia, Wang, Shuai, Feng, Litong, Xia, Gui-Song, He, Conghui
Publication Year: 2024
Collection: Computer Science
Subject Terms: Computer Science - Computer Vision and Pattern Recognition
More Details: This paper develops a Versatile and Honest vision language Model (VHM) for remote sensing image analysis. VHM is built on a large-scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest instruction dataset comprising both factual and deceptive questions (HnstD). Unlike prevailing remote sensing image-text datasets, in which image captions focus on a few prominent objects and their relationships, VersaD captions provide detailed information about image properties, object attributes, and the overall scene. This comprehensive captioning enables VHM to thoroughly understand remote sensing images and perform diverse remote sensing tasks. Moreover, different from existing remote sensing instruction datasets that only include factual questions, HnstD contains additional deceptive questions stemming from the non-existence of objects. This feature prevents VHM from producing affirmative answers to nonsense queries, thereby ensuring its honesty. In our experiments, VHM significantly outperforms various vision language models on common tasks of scene classification, visual question answering, and visual grounding. Additionally, VHM achieves competent performance on several unexplored tasks, such as building vectorizing, multi-label classification and honest question answering. We will release the code, data and model weights at https://github.com/opendatalab/VHM .
Comment: Equal contribution: Chao Pang, Xingxing Weng, Jiang Wu; Corresponding author: Gui-Song Xia, Conghui He
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2403.20213
Accession Number: edsarx.2403.20213
Database: arXiv
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