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Human-in-the-Loop Fine-Grained Visual
    Categorization using Visipedia
                  or
   "The Revolution will be C...
2
Birds-200 Dataset




6033 images over 200 bird species
4
MTurker Label Certainty




                          5
Visual 20 Questions




• “Computer Vision” module = Vedaldi’s VLFeat
• VQ Geometric Blur, color/gray SIFT spatial pyramid...
7
General Observations
• User Responses are Stochastic
• Computer Vision Reduces Manual Labor
• User Responses Drive Up Perf...
w/o Computer Vision




• User Responses are Stochastic
                              9
w/ Computer Vision




• Computer Vision Reduces Manual Labor
                                 10
w/ Computer Vision (cont’d)




• User Responses Drive Up Performance
                                11
• Computer Vision Improves Overall Performance
• Different Questions are Asked w/ and w/o
  Computer Vision
• Recognition is not Always Successful
Indigo Bunting   Blue Grosbeak
Fcv the revolution will be curated: human in the loop fine grained visual categorization using visipedia belongie
Fcv the revolution will be curated: human in the loop fine grained visual categorization using visipedia belongie
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Fcv the revolution will be curated: human in the loop fine grained visual categorization using visipedia belongie

  1. 1. Human-in-the-Loop Fine-Grained Visual Categorization using Visipedia or "The Revolution will be Curated" Serge Belongie UC San Diego Steve Branson Catherine Wah Peter Welinder Boris Babenko Pietro Perona Florian Schroff
  2. 2. 2
  3. 3. Birds-200 Dataset 6033 images over 200 bird species
  4. 4. 4
  5. 5. MTurker Label Certainty 5
  6. 6. Visual 20 Questions • “Computer Vision” module = Vedaldi’s VLFeat • VQ Geometric Blur, color/gray SIFT spatial pyramid • Multiple Kernel Learning • Per-Class 1-vs-All SVM • 15 training examples per bird species • Choose question to maximize expected Information Gain 6
  7. 7. 7
  8. 8. General Observations • User Responses are Stochastic • Computer Vision Reduces Manual Labor • User Responses Drive Up Performance • Computer Vision Improves Overall Performance • Different Questions are Asked w/ and w/o Computer Vision • Recognition is not Always Successful 8
  9. 9. w/o Computer Vision • User Responses are Stochastic 9
  10. 10. w/ Computer Vision • Computer Vision Reduces Manual Labor 10
  11. 11. w/ Computer Vision (cont’d) • User Responses Drive Up Performance 11
  12. 12. • Computer Vision Improves Overall Performance • Different Questions are Asked w/ and w/o Computer Vision
  13. 13. • Recognition is not Always Successful
  14. 14. Indigo Bunting Blue Grosbeak
  • pacoid

    Dec. 28, 2017
  • bwrasa

    Oct. 23, 2016

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