MMA estimation on multi-modal images in SEN12MS dataset.

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• MMA estimation on different multi-modal image datasets. The modal of the images: (1) RGB-NIR, (2) Optical-SAR, (3) Optical-SAR, (4) T1-T2 (5) RGB-Depth, (6) UV-Green.
  by: Yide Di (20969124)
  Published: (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.
  by: Yide Di (20969124)
  Published: (2025)
• 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.
  by: Yide Di (20969124)
  Published: (2025)
• Examples of the image pairs of different modals. (a) The SEN 12 MS dataset. a1. Optical a2. SAR a3. NIR a4. SWIR (b) The RGB-NIR Scene dataset. b1. RGB b2. Near-Infrared (c) The WHU-OPT-SAR dataset. c1. Optical c2. SAR (d) The Optical-SAR dataset. d1. Optical d2. SAR (e) The BrainWeb dataset. e1. T1 e2. T2 (f) The NYU-Depth V2 dataset. f1. Depth f2. RGB (g) The UV/Green Image dataset. g1. UV g2. Green.
  by: Yide Di (20969124)
  Published: (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.
  by: Yide Di (20969124)
  Published: (2025)