The overview of UFM and the MIA transformer. The UFM model encompasses all the processes involved in image enhancement, feature extraction, coarse matching, and fine matching. The MIA Transformer is utilized in both the coarse and fine matching stages.

مواد مشابهة

• Illustration of feature matching of UFM. When working with specific data, the pre-trained backbone is frozen, and only the corresponding modal assistants need to be fine-tuned for feature matching. The multi-modal assistants contain both same-modal assistants and different-modal matching assistants.
  حسب: Yide Di (20969124)
  منشور في: (2025)
• Feature matching of same-modal images. (a) SuperGlue (b) MatchFomer (c) LoFTR (d) UFM. The matches with less than 1 pixel error are represented by lines.
  حسب: Yide Di (20969124)
  منشور في: (2025)
• Fine-tuning on same-model feature matching tasks. The X-FFN and Y-FFN represent the assistants of any two kinds of pre-trained different modal images in the second stage of Fig 4. The fine-tuning of the X-modal image and the fine-tuning of the Y-modal image are independent of each other.
  حسب: Yide Di (20969124)
  منشور في: (2025)
• Feature matching on images with very little texture.
  حسب: Yide Di (20969124)
  منشور في: (2025)
• Feature matching of different-modals images: (a) MatchosNet (b) LoFTR (c) FeMIP, and (d) UFM. The datasets from top to bottom are: SEN 12 MS dataset, RGB-NIR Scene dataset, WHU-OPT-SAR dataset, Optical-SAR dataset, BrainWeb dataset, NYU-Depth V2 dataset and UV-Green dataset. The images of SEN 12 MS dataset, RGB-NIR Scene dataset and WHU-OPT-SAR dataset are rotated and the images of the Optical-SAR dataset and the NYU-Depth V2 dataset are cropped. The matches with less than 1 pixel error are represented by lines.
  حسب: Yide Di (20969124)
  منشور في: (2025)