O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.
Human-in-the-Loop Fine-Grained Visual    Categorization using Visipedia                  or   "The Revolution will be Cura...
2
Birds-200 Dataset6033 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• Mult...
7
General Observations• User Responses are Stochastic• Computer Vision Reduces Manual Labor• User Responses Drive Up Perform...
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
Próximos SlideShares
Carregando em…5
×

Fcv the revolution will be curated: human in the loop fine grained visual categorization using visipedia belongie

494 visualizações

Publicada em

Publicada em: Tecnologia, Educação
  • Seja o primeiro a comentar

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 Dataset6033 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

×