FAIR1M-GQA: Fine-Grained Grounded Question Answering Dataset in Remote Sensing
Author:
Affiliation:
1. Chinese Academy of Sciences,Aerospace Information Research Institute,Beijing,China
2. Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642697.pdf?arnumber=10642697
Reference7 articles.
1. On the opportunities and risks of foundation models;Bommasani,2021
2. RSVQA: Visual Question Answering for Remote Sensing Data
3. RSVQA Meets Bigearthnet: A New, Large-Scale, Visual Question Answering Dataset for Remote Sensing
4. FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery
5. Shikra: Unleashing multi-modal llm’s referential dialogue magic;Chen,2023
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