The parameter setting of the evolutionary neural network. حسب Wenbing Shi (5806160)

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The fitness of ANEAT model evolution. حسب Wenbing Shi (5806160)

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The structure of the ANEAT model corresponds to the optimal species genome. حسب Wenbing Shi (5806160)

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The construction process of the CGO prediction model. حسب Wenbing Shi (5806160)

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The comparison of different prediction methods on the test set. حسب Wenbing Shi (5806160)

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The genome recombination of neuroevolution. حسب Wenbing Shi (5806160)

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The fitness of NANEAT model evolution. حسب Wenbing Shi (5806160)

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The speciation of NANEAT model evolution. حسب Wenbing Shi (5806160)

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Feature matching on images with very little texture. حسب Yide Di (20969124)

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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... حسب Yide Di (20969124)

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Illustration of pre-training. The pre-training of NIR and SAR images is taken here as an example. Pre-training consists of 3 stages: (1) pre-train the general FFN, (2) pre-train al... حسب Yide Di (20969124)

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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. حسب Yide Di (20969124)

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Examples of the image pairs of the same modal. (a) The Hpatches dataset. (b) The InLoc dataset. (c) The Aachen Day-Night v1.1 dataset. حسب Yide Di (20969124)

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Data augmentation is applied both geometrically and in terms of intensity. Geometrically, the images are mirrored, flipped, rotated, and randomly cropped. For intensity augmentatio... حسب Yide Di (20969124)

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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)

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MMA estimation on multi-modal images in SEN12MS dataset. حسب Yide Di (20969124)

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Fine-tuning on different-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 Fi... حسب Yide Di (20969124)

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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... حسب Yide Di (20969124)

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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... حسب Yide Di (20969124)

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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.... حسب Yide Di (20969124)

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