Search alternatives:
"data augmentations algorithm" » "data augmentation algorithms" (Expand Search), "data segmentation algorithm" (Expand Search), "data segmentation algorithms" (Expand Search), "data augmentation algorithm" (Expand Search), "dataset augmentation algorithm" (Expand Search)
Showing 1 - 18 results of 18 for search '"data augmentations algorithm"', query time: 0.16s Refine Results
  1. 1
    AugPerSumm: Augmented User Behavior Trajectory Data Collection for Personalized Summarization

    AugPerSumm: Augmented User Behavior Trajectory Data Collection for Personalized Summarization by Parthiv Chatterjee (22407961)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  2. 2
    Feature matching on images with very little texture.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  3. 3
    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.

    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... by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  4. 4
    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 all X-X assistants, and (3) pre-train all X-Y assistants.

    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... by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  5. 5
    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.

    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
    Subjects:
    Save to List
    Saved in:
  6. 6
    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.

    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. by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  7. 7
    Data augmentation is applied both geometrically and in terms of intensity. Geometrically, the images are mirrored, flipped, rotated, and randomly cropped. For intensity augmentation, random noise is added, and random masking is applied. Finally, a square matrix (GT matrix) is used to represent the correspondence of matching points between the two images. The GT_matrix is a square matrix of <i>N</i> × <i>N</i> dimensions. <i>GT</i> ⁡  ( <i>i</i> , <i>j</i> )  represents the element of the <i>i</i>th row and the <i>j</i>th column in the GT matrix. The shown input image pairs take optical and SAR image pairs as an example.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  8. 8
    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.

    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. by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  9. 9
    MMA estimation on multi-modal images in SEN12MS dataset.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  10. 10
    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 Fig 4. The X-Y FFN represent the assistant of the pre-trained different modal images in the third stage of Fig 4.

    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... by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  11. 11
    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.

    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... by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  12. 12
    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.

    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... by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  13. 13
    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.

    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.... by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  14. 14
    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.

    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 WH... by Yide Di (20969124)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  15. 15
    The illustration of the core idea of DACB-PolarMix.

    The illustration of the core idea of DACB-PolarMix. by Bo Liu (127343)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  16. 16
    Visualization of comparative experiment results. The content within the red line box demonstrates the various performances of semantic segmentation.

    Visualization of comparative experiment results. The content within the red line box demonstrates the various performances of semantic segmentation. by Bo Liu (127343)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  17. 17
    The visualization of a 3D point cloud.

    The visualization of a 3D point cloud. by Bo Liu (127343)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  18. 18
    The illustration of instance-level operation of PolarMix.

    The illustration of instance-level operation of PolarMix. by Bo Liu (127343)

    Published 2025
    Subjects:
    Save to List
    Saved in:

Search Tools: