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Object Classes Most recent work is at the object level

Object Classes Most recent work is at the object level. We want in addition: 1. Individual Recognition. 2. Object parts and sub-parts Called: Full Interpretation. Window. Mirror. Window. Door knob. Headlight. Back wheel. Bumper. Front wheel. Headlight.

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Object Classes Most recent work is at the object level

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  1. Object ClassesMost recent work is at the object level

  2. We want in addition: 1. Individual Recognition

  3. 2. Object parts and sub-partsCalled: Full Interpretation Window Mirror Window Door knob Headlight Back wheel Bumper Front wheel Headlight

  4. 3. Action recognition (which 2 are different?)

  5. 4. Agents Interactions 3 1 2 4 5 6

  6. Class Non-class

  7. Class Non-class

  8. Is this an airplane?

  9. Unsupervised Training Data

  10. Features and Classifiers In DNN -- the net produces features of the top layer Previous work explored a broad range of features

  11. Features used in the past: Generic Features Simple (wavelets) Complex (Geons)

  12. Marr-Nishihara

  13. Marr Net 2017 rotated versions of the object in the image

  14. Past Class-specific Features: Common Fragments

  15. Optima Features: Mutual Information I(C,F) Class: 1 1 0 1 0 1 0 0 Feature: 1 0 0 1 1 1 0 0 I(F,C) = H(C) – H(C|F)

  16. Star model Detected fragments ‘vote’ for the center location Find location with maximal vote In variations, a popular state-of-the art scheme

  17. Hierarchies of sub-fragments(a ‘deep net’) Detect the part itself by simpler sub-parts Repeat at multiple levels, to obtain a hierarchy of parts and sub-parts

  18. Example Hierarchies

  19. Classification by Features Hierarchy c x2 X1 X3 X4 X5 p(c,X,F) = p(c)Πp(xi|xi-) p(Fi|xi)

  20. Global optimum can be found by max-sum message passing (two-pass computation) c x2 X1 X3 X4 X5 X = argmax [p(c,X,F) = p(c)Πp(xi|xi-) p(Fi|xi) ]

  21. Context: Parts and ObjectsResults of two-pass computation

  22. Current use of probabilistic graph models

  23. HoG Descriptor Dallal, N & Triggs, B. Histograms of Oriented Gradients for Human Detection SIFT is similar, different details, multi-scale

  24. SVM – linear separation in feature space

  25. Optimal Separation SVM Perceptron The Nature of Statistical Learning Theory, 1995 Rosenblatt, Principles of Neurodynamics 1962. Find a separating plane such that the closest points are as far as possible

  26. +1 The Margin -1 0 Separating line: w ∙ x + b = 0 Far line: w ∙ x + b = +1 Their distance: w ∙ ∆x = +1 Separation: |∆x| = 1/|w| Margin: 2/|w|

  27. Using patches with HoG descriptors and classification by SVM Person model: HoG

  28. DPM: Adding Parts

  29. Bicycle model: root, parts, spatial map Person model

  30. Deep Learning

  31. ImageNet

  32. AlexNet

  33. On the history deep learning

  34. A Neural Network Model A network of ‘neurons’ with multiple layers Repeating structure, linear, non-linear Automatic learning of weights between units

  35. The McCulloch–Pitts neuron (1943) Relu

  36. Perceptron learning yj = f(xj)

  37. Back-propagation 1986

  38. LeNet 1998 Essentially the same as the current generation

  39. MNIST data set

  40. Hinton Trends in Cognitive Science 2007 The goal: unsupervised Restricted Boltzmann Machines Combining generative model and inference CNN are feed-forward and massively supervised

  41. Basic structure of deep nets. Not detailed here, but make sure you know the layers structure and repeating 3-layer arrangement

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