title: SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image
    Interpretation
summary: In the field of synthetic aperture radar (SAR) remote sensing image
  interpretation, although Vision language models (VLMs) have made remarkable
  progress in natural language processing and image understanding, their
  applications remain limited in professional domains due to insufficient domain
  expertise. This paper innovatively proposes the first large-scale multimodal
  dialogue dataset for SAR images, named SARChat-2M, which contains approximately
  2 million high-quality image-text pairs, encompasses diverse scenarios with
  detailed target annotations. This dataset not only supports several key tasks
  such as visual understanding and object detection tasks, but also has unique
  innovative aspects: this study develop a visual-language dataset and benchmark
  for the SAR domain, enabling and evaluating VLMs' capabilities in SAR image
  interpretation, which provides a paradigmatic framework for constructing
  multimodal datasets across various remote sensing vertical domains. Through
  experiments on 16 mainstream VLMs, the effectiveness of the dataset has been
  fully verified, and the first multi-task dialogue benchmark in the SAR field
  has been successfully established. The project will be released at
  https://github.com/JimmyMa99/SARChat, aiming to promote the in-depth
  development and wide application of SAR visual language models.
paper_authors._id: 67ad5f32d1a5243cc4fa38ad
paper_authors.name: Zhiming Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:21:57.239Z
paper_authors._id: 64a0ed5ed5374ca472cfb0ac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64a0ed5ed5374ca472cfb0ac/n_wXamXfR_PPn0hRbnR1X.jpeg
paper_authors.fullname: ZhimingMa
paper_authors.type: user
paper_authors.user: JimmyMa99
paper_authors._id: 67ad5f32d1a5243cc4fa38ae
paper_authors.name: Xiayang Xiao
paper_authors._id: 67ad5f32d1a5243cc4fa38af
paper_authors.name: Sihao Dong
paper_authors._id: 67ad5f32d1a5243cc4fa38b0
paper_authors.name: Peidong Wang
paper_authors._id: 67ad5f32d1a5243cc4fa38b1
paper_authors.name: HaiPeng Wang
paper_authors._id: 67ad5f32d1a5243cc4fa38b2
paper_authors.name: Qingyun Pan
paper_title: SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image
    Interpretation
paper_summary: In the field of synthetic aperture radar (SAR) remote sensing image
  interpretation, although Vision language models (VLMs) have made remarkable
  progress in natural language processing and image understanding, their
  applications remain limited in professional domains due to insufficient domain
  expertise. This paper innovatively proposes the first large-scale multimodal
  dialogue dataset for SAR images, named SARChat-2M, which contains approximately
  2 million high-quality image-text pairs, encompasses diverse scenarios with
  detailed target annotations. This dataset not only supports several key tasks
  such as visual understanding and object detection tasks, but also has unique
  innovative aspects: this study develop a visual-language dataset and benchmark
  for the SAR domain, enabling and evaluating VLMs' capabilities in SAR image
  interpretation, which provides a paradigmatic framework for constructing
  multimodal datasets across various remote sensing vertical domains. Through
  experiments on 16 mainstream VLMs, the effectiveness of the dataset has been
  fully verified, and the first multi-task dialogue benchmark in the SAR field
  has been successfully established. The project will be released at
  https://github.com/JimmyMa99/SARChat, aiming to promote the in-depth
  development and wide application of SAR visual language models.
paper_ai_summary: A large-scale multimodal dialogue dataset, SARChat-2M, for SAR images supports VLMs in diverse visual understanding and object detection tasks, establishing a multi-task dialogue benchmark in the SAR field.
paper_ai_keywords: multimodal dialogue SAR images visual-language models image-text pairs target annotations visual understanding object detection multimodal datasets remote sensing dialogue benchmark