【授業スライド】AnonyMask

慶應義塾⼤学 B4 和田唯我 / Yuiga Wada
授業『機械知能』のスライドです. (院先取り)

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- Blog

Transcript

1. AnonyMask
   XXXXXX Yuiga Wada YYYYY Seitaro Otsuki ZZZZZZ Yui Iioka
   Machine Intelligence Demonstration (Team-7)
   1
   https://github.com/OtsuKotsu/Anonymask
   

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2. AnonyMask : Remove Corporate Logo naturally
   ● Background
   ○ Huge cost to hide the logos
   ○ Existing approaches result in unnatural images!
   ● Our product : AnonyMask
   ○ Detects the logo by the object detection model
   ○ Masks it out and repaints masked region naturally
   2
   

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3. Method : AnonyMask
   ● Overall flow
   1. Object detection
   2. Masking & Reconstruction
   3. Smoothing & Improving Resolution
   3
   

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4. Method : AnonyMask
   YOLOv5
   Masked Auto Encoder
   SwinIR
   4
   

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5. Results : AnonyMask generates remarkably natural images 👀
   5
   

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6. Demonstration
   https://drive.google.com/file/d/1n0Ir2d-zZ9MazjbOpYz54ibGo540elnH/view?usp=sharing
   6
   

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7. Quantitative Result
   Recall Precision F-Score mAP@0.5 mAP@0.5:0.95
   val. 0.656 0.789 0.716 0.732 0.507
   test 0.653 0.779 0.710 0.728 0.500
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8. Thank you for listening
   Thank you for listening !
   https://github.com/OtsuKotsu/Anonymask
   8
   

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9. Appendix : Method (1/4)
   ● YOLO v5
   ○ Detects the position and type of objects
   ○ Metrics : mAP (mean Average Precision)
   ● Overall flow
   1. Object detection
   2. Masking & Reconstruction
   3. Smoothing & Improving Resolution
   9
   

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10. Appendix : Method (2/4)
    ● Overall flow
    1. Object detection
    2. Masking & Reconstruction
    3. Smoothing & Improving Resolution
    ● MAE [He+, CVPR22]
    ○ Masks the logo and Reconstructs from
    surrounding pixels
    ○ Metrics : MSE
    10
    

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11. Appendix : Method (3/4)
    ● Overall flow
    1. Object detection
    2. Masking & Reconstruction
    3. Smoothing & Improving Resolution
    ● Gaussian filter
    ○ Erases noise in an image
    ○ Blurs images
    11
    

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12. 12
    Appendix : Method (4/4)
    ● Overall flow
    1. Object detection
    2. Masking & Reconstruction
    3. Smoothing & Improving Resolution
    ● SwinIR [Liang+, ICCV21]
    ○ Restores high-quality images from
    low-quality images
    

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13. Appendix : Dataset
    13
    QMUL-OpenLogo
    ● 27,083 images from 352 logo classes
    ● Object-level Annotations
    Our split :
    https://hangsu0730.github.io/qmul-openlogo/
    #Examples
    Training set 18752
    Validation set 4165
    Test set 4166
    

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14. Appendix : demo details (1/4)
    YOLOv5
    MAE
    SwinIR
    Server
    Client
    https://XXXX:port
    14
    

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15. Appendix : demo details (2/4)
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16. Appendix : demo details (3/4)
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17. Appendix : demo details (4/4)
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18. Reference
    1. K. He, X. Chen, S. Xie, et al., “Masked autoencoders are scalable vision learners,” CVPR,
    pp.16000–16009, 2022.
    2. J. Liang, J. Cao, G. Sun, et al., “Swinir: Image restoration using swin transformer,” CVPR,
    pp.1833–1844, 2021.
    3. G. Jocher, A. Chaurasia, A. Stoken, et al., “ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU
    and OpenVINO Export and Inference,” 2022. https://doi.org/10.5281/zenodo.6222936
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