Describing People: A Poselet-BasedApproach to Attribute Classification               Lubomir Bourdev1,2                Sub...
Goal: Extract attributes from      images of people
Who has long hair?
Who has short pants?
Male or female?
Prior workon poselets and on attributes
Prior work on Poselets•   Introduced by [Bourdev and Malik, ICCV09]•   Detection with poselets [Bourdev et al, ECCV10]•   ...
Prior work on Poselets•   Introduced by [Bourdev and Malik, ICCV09]•   Detection with poselets [Bourdev et al, ECCV10]•   ...
Prior work on Poselets•   Introduced by [Bourdev and Malik, ICCV09]•   Detection with poselets [Bourdev et al, ECCV10]•   ...
Prior work on Poselets•   Introduced by [Bourdev and Malik, ICCV09]•   Detection with poselets [Bourdev et al, ECCV10]•   ...
Prior work on Poselets•   Introduced by [Bourdev and Malik, ICCV09]•   Detection with poselets [Bourdev et al, ECCV10]•   ...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Prior work on AttributesAttributes as intermediate parts                 Attributes and actionsDiscovering attributes from...
Prior work on AttributesAttributes as intermediate parts                 Image retrieval with attributesDiscovering attrib...
Poseletsfor Attribute Classification
Male or female?
Gender recognition is easier if we      factor out the pose
Poselets      [Bourdev & Malik ICCV09]
PoseletsExamples may differ visually but have common semantics
How do we train a poselet?
Finding correspondences at training timeGiven part of a human   How do we find a similarpose                    pose confi...
Finding correspondences at training time                  Left Shoulder                  Left HipWe use keypoints to annot...
Finding correspondences at training time          Residual Error
Training poselet classifiersResidual   0.15   0.20   0.10    0.85   0.15    0.35Error:1.   Given a seed patch2.   Find the...
Training poselet classifiers1.   Given a seed patch2.   Find the closest patch for every other person3.   Sort them by res...
Attribute Classification Algorithm           at Test Time
Goal: Extract attributes of this person
Goal: Extract attributes of this person              Target person bounds     Input:              Bounds of other nearby p...
Step 1: Detect poselet activations                 [Bourdev et al, ECCV10]
Step 2: Cluster the activations                [Bourdev et al, ECCV10]
Step 3: Predict person bounds               [Bourdev et al, ECCV10]
Step 4: Identify the correct cluster                 Max-flow in bipartite graph
Start with its poselet activationsPoseletActivations
Features •   Pyramid HOG •   LAB histogram •   Skin features     •     Hands-skin     •     Legs-skin                     ...
Attribute Classification OverviewPoselet-levelAttributeClassifiersFeaturesPoseletActivations
Attribute Classification OverviewPerson-levelAttributeClassifiersPoselet-levelAttributeClassifiersFeaturesPoseletActivations
Attribute Classification OverviewContext-levelAttributeClassifiersPerson-levelAttributeClassifiersPoselet-levelAttributeCl...
Results
Our dataset•   Source: VOC 2010 trainval for Person + H3D•   ~8000 annotations (4000 train + 4000 test)•   9 binary attrib...
Visual search on our test set“Wears hat”“Female”
“Has long hair”“Wears glasses”
“Wears shorts”“Has long sleeves”
“Doesn’t have long sleeves”
Our baseline•   Canny-modulated HOG with SPM kernel [Lazebnik et al CVPR06]•   To help the baseline trained separate SPM f...
Precision/recall on our test setLabel     -   ---frequencySPM          ___No        ___contextFull      ___Model
State-of-the-art Gender Recognition• We outperform Cognitec (top-notch face  recognizer)• We outperform any gender recogni...
Confusions                                        long hairMen most confused as womenWomen most confused as men   baseball...
annotationNon-T-shirt most confused to be T-shirt          errorsShort pants most confused to be long pants         Are th...
Best poselets per attributeGender:Long Hair:Wears glasses:
We can describe a picture of a person                  “A woman with long hair,                  glasses and long pants”(??)
Conclusion
How poselets help in high-level vision The image is a complex      Poselets decouple pose andfunction of the viewpoint,   ...
Google “poselets” to get:•   The set of published poselet papers•   H3D data set + Matlab tools•   Java3D annotation tool ...
Poselets website                          Failure modehttp://eecs.berkeley.edu/~lbourdev/poselets hair,                   ...
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  • Describing People: A Poselet-based approach to attribute classification

    1. 1. Describing People: A Poselet-BasedApproach to Attribute Classification Lubomir Bourdev1,2 Subhransu Maji1 Jitendra Malik1 1EECS U.C. Berkeley 2Adobe Systems Inc.
    2. 2. Goal: Extract attributes from images of people
    3. 3. Who has long hair?
    4. 4. Who has short pants?
    5. 5. Male or female?
    6. 6. Prior workon poselets and on attributes
    7. 7. Prior work on Poselets• Introduced by [Bourdev and Malik, ICCV09]• Detection with poselets [Bourdev et al, ECCV10]• Applications • Segmentation [Brox et al, ECCV10] [Maire et al, ICCV 11] • Actions [Yang et al, CVPR10] [Maji et al, CVPR11] [Yao et al, ICCV11] • Human parsing [Wang et al, CVPR11] • Semantic contours [Hariharan et al, ICCV11] • Subordinate level categorization [Farrell et al, ICCV11]
    8. 8. Prior work on Poselets• Introduced by [Bourdev and Malik, ICCV09]• Detection with poselets [Bourdev et al, ECCV10]• Applications • Segmentation [Brox et al, ECCV10] [Maire et al, ICCV 11] • Actions [Yang et al, CVPR10] [Maji et al, CVPR11] [Yao et al, ICCV11] • Human parsing [Wang et al, CVPR11] • Semantic contours [Hariharan et al, ICCV11] • Subordinate level categorization [Farrell et al, ICCV11]
    9. 9. Prior work on Poselets• Introduced by [Bourdev and Malik, ICCV09]• Detection with poselets [Bourdev et al, ECCV10]• Applications • Segmentation [Brox et al, ECCV10] [Maire et al, ICCV 11] • Actions [Yang et al, CVPR10] [Maji et al, CVPR11] [Yao et al, ICCV11] • Human parsing [Wang et al, CVPR11] • Semantic contours [Hariharan et al, ICCV11] • Subordinate level categorization [Farrell et al, ICCV11]
    10. 10. Prior work on Poselets• Introduced by [Bourdev and Malik, ICCV09]• Detection with poselets [Bourdev et al, ECCV10]• Applications • Segmentation [Brox et al, ECCV10] [Maire et al, ICCV 11] • Actions [Yang et al, CVPR10] [Maji et al, CVPR11] [Yao et al, ICCV11] • Human parsing [Wang et al, CVPR11] • Semantic contours [Hariharan et al, ICCV11] • Subordinate level categorization [Farrell et al, ICCV11]
    11. 11. Prior work on Poselets• Introduced by [Bourdev and Malik, ICCV09]• Detection with poselets [Bourdev et al, ECCV10]• Applications • Segmentation [Brox et al, ECCV10] [Maire et al, ICCV 11] • Actions [Yang et al, CVPR10] [Maji et al, CVPR11] [Yao et al, ICCV11] • Human parsing [Wang et al, CVPR11] • Semantic contours [Hariharan et al, ICCV11] • Subordinate level categorization [Farrell et al, ICCV11]
    12. 12. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    13. 13. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11][Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    14. 14. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    15. 15. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    16. 16. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    17. 17. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    18. 18. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al,CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Bransonel al, ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al,CVPR11] [Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al,ICCV11] [Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    19. 19. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    20. 20. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    21. 21. Prior work on AttributesAttributes as intermediate parts Attributes and actionsDiscovering attributes from text Active learning with attributesDiscovering attributes from images Attributes of peopleAttributes from motion capture Gender attributeJoint learning of classes & attributesImage retrieval with attributes[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    22. 22. Prior work on AttributesAttributes as intermediate parts Image retrieval with attributesDiscovering attributes from text Attributes and actionsDiscovering attributes from images Active learning with attributesAttributes from motion capture Attributes of peopleJoint learning of classes & attributes Gender attribute[Cottrell and Medcalfe, NIPS90] [Golomb et al, NIPS90] [Moghaddam& Yang, PAMI02][Ferrari &Zisserman, NIPS07] [Kumar et al, ECCV08] [Gallagher and Chen, CVPR08][Cao et al, ACM08] [Lampert et al, CVPR09] [Farhadi et al, CVPR 09] [Wang et al,BMVC09] [Wang and Forsyth, ICCV09] [Kumar et al, ICCV09] [Farhadi et al, CVPR10][Berg et al, ECCV10] [Wang and Mori, ECCV10] [Sigal et al, ECCV10] [Branson el al,ECCV10] [Hwang et al, CVPR11] [Parikh and Grauman, CVPR11] [Douze et al, CVPR11][Kovashka et al, ICCV11] [Liu et al, CVPR11] [Qiu et al, ICCV11] [Yao et al, ICCV11][Dhar et al, CVPR11] [Parikh and Grauman, ICCV11] [Siddiquie et al, CVPR11]
    23. 23. Poseletsfor Attribute Classification
    24. 24. Male or female?
    25. 25. Gender recognition is easier if we factor out the pose
    26. 26. Poselets [Bourdev & Malik ICCV09]
    27. 27. PoseletsExamples may differ visually but have common semantics
    28. 28. How do we train a poselet?
    29. 29. Finding correspondences at training timeGiven part of a human How do we find a similarpose pose configuration in the training set?
    30. 30. Finding correspondences at training time Left Shoulder Left HipWe use keypoints to annotate the joints, eyes, nose, etc. of people
    31. 31. Finding correspondences at training time Residual Error
    32. 32. Training poselet classifiersResidual 0.15 0.20 0.10 0.85 0.15 0.35Error:1. Given a seed patch2. Find the closest patch for every other person3. Sort them by residual error4. Threshold them
    33. 33. Training poselet classifiers1. Given a seed patch2. Find the closest patch for every other person3. Sort them by residual error4. Threshold them5. Use them as positive training examples to train a linear SVM with HOG features
    34. 34. Attribute Classification Algorithm at Test Time
    35. 35. Goal: Extract attributes of this person
    36. 36. Goal: Extract attributes of this person Target person bounds Input: Bounds of other nearby people
    37. 37. Step 1: Detect poselet activations [Bourdev et al, ECCV10]
    38. 38. Step 2: Cluster the activations [Bourdev et al, ECCV10]
    39. 39. Step 3: Predict person bounds [Bourdev et al, ECCV10]
    40. 40. Step 4: Identify the correct cluster Max-flow in bipartite graph
    41. 41. Start with its poselet activationsPoseletActivations
    42. 42. Features • Pyramid HOG • LAB histogram • Skin features • Hands-skin • Legs-skin Poselet Skin Arms B .* C patch mask maskFeaturesPoseletActivations
    43. 43. Attribute Classification OverviewPoselet-levelAttributeClassifiersFeaturesPoseletActivations
    44. 44. Attribute Classification OverviewPerson-levelAttributeClassifiersPoselet-levelAttributeClassifiersFeaturesPoseletActivations
    45. 45. Attribute Classification OverviewContext-levelAttributeClassifiersPerson-levelAttributeClassifiersPoselet-levelAttributeClassifiersFeaturesPoseletActivations
    46. 46. Results
    47. 47. Our dataset• Source: VOC 2010 trainval for Person + H3D• ~8000 annotations (4000 train + 4000 test)• 9 binary attributes specified by 5 independent annotators via AMT• Ground truth label: If 4 of the 5 agree• Dataset will be made publicly available
    48. 48. Visual search on our test set“Wears hat”“Female”
    49. 49. “Has long hair”“Wears glasses”
    50. 50. “Wears shorts”“Has long sleeves”
    51. 51. “Doesn’t have long sleeves”
    52. 52. Our baseline• Canny-modulated HOG with SPM kernel [Lazebnik et al CVPR06]• To help the baseline trained separate SPM for four viewpoints: Full view Head zoom Upper body Legs• For each attribute we pick the best SPM as our baseline
    53. 53. Precision/recall on our test setLabel - ---frequencySPM ___No ___contextFull ___Model
    54. 54. State-of-the-art Gender Recognition• We outperform Cognitec (top-notch face recognizer)• We outperform any gender recognizer based on frontal faces (are there others?) • 61% of our test have frontal faces. • Even with perfect classification of frontal faces, max AP=80.5% vs. our AP of 82.4%
    55. 55. Confusions long hairMen most confused as womenWomen most confused as men baseball hat hair hidden
    56. 56. annotationNon-T-shirt most confused to be T-shirt errorsShort pants most confused to be long pants Are these pants short? wrong person occlusion
    57. 57. Best poselets per attributeGender:Long Hair:Wears glasses:
    58. 58. We can describe a picture of a person “A woman with long hair, glasses and long pants”(??)
    59. 59. Conclusion
    60. 60. How poselets help in high-level vision The image is a complex Poselets decouple pose andfunction of the viewpoint, camera view from pose, appearance, etc. appearance
    61. 61. Google “poselets” to get:• The set of published poselet papers• H3D data set + Matlab tools• Java3D annotation tool + video tutorial• Matlab code to detect people using poselets• Our latest trained poselets
    62. 62. Poselets website Failure modehttp://eecs.berkeley.edu/~lbourdev/poselets hair, “A man with with long “A woman short “Aglasses,with short hair, “Aperson short short hair, man with sleeves and hair and long sleeves”• The set of published poseletno hat pants” sleeves glasses, short sleeves” papers and long long• H3D data set + Matlab toolsand person with “A shorts” Java3D annotation tool + video tutorial longcomputer vision “A pants”•• Matlab code to detect people using poselets professor who likes• Our latest trained poselets machine learning”
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