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Alan Yuille (UCLA & Korea University) Leo Zhu (NYU/UCLA) & Yuanhao Chen (UCLA) Y. Lin, C. Lin, Y. Lu (Microsoft Beijing) A. Torrabla and W. Freeman (MIT)

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Presentation on theme: "Alan Yuille (UCLA & Korea University) Leo Zhu (NYU/UCLA) & Yuanhao Chen (UCLA) Y. Lin, C. Lin, Y. Lu (Microsoft Beijing) A. Torrabla and W. Freeman (MIT)"— Presentation transcript:

1 Alan Yuille (UCLA & Korea University) Leo Zhu (NYU/UCLA) & Yuanhao Chen (UCLA) Y. Lin, C. Lin, Y. Lu (Microsoft Beijing) A. Torrabla and W. Freeman (MIT)

2  A unified framework for vision in terms of probability distributions defined on graphs.  Related to Pattern Theory. Grenander, Mumford, Geman, SC Zhu.  Related to Machine Learning….  Related to Biologically Inspired Models… 2

3 (1) Image Labeling: Segmentation and Object Detection. Datasets: MSRC, Pascal VOC07. Zhu, Chen, Lin, Lin, Yuille (2008,2011) (2) Object Category Detection. Datasets: Pascal 2010, earlier Pascal Zhu, Chen, Torrabla, Freeman, Yuille (2010) (3) Multi-Class,-View,-Pose. Datasets: Baseball Players, Pascal, LableMe. Zhu, Chen, Lin, Lin, Yuille (2008,2011) Zhu, Chen, Torrabla, Freeman, Yuille (2010) 3

4  Probability Distributions defined over structured representations.  General Framework for all Intelligence?  Graph Structure and State Variables. Knowledge Representation.  Probability Distributions.  Computation: Inference Algorithms. Learning Algorithms. 4

5  Goal: Label each image pixel as `sky, road, cow,…’ E.g. 21 labels.  Combines segmentation with primitive object recognition.  Zhu, Chen, Lin, Lin, Yuille 2008,

6 6  Hierarchical Graph (Quadtree).  Variables – Segmentation-recognition templates.

7  Executive Summary: State variables have same complexity at all levels. 7 Global: top-level summary of scene e.g. object layout Local: more details about shape and appearance coarse to fine

8  (1) Captures short-, medium-, long- range context.  (2) Enables efficient hierarchical compositional inference.  (3) Coarse-to-fine representation of image (executive summary).  Note: groundtruth evaluations only rank fine scale representation. 8

9  X: input image.  Y State Variables of all nodes of the Graph:  Energy E(x,y) contains: (i) Prior terms – relations between state variables Y independent of the image X. (ii) Data terms – relation between state variables Y and image X. 9

10 10 f: appearance likelihood g:object layout prior homogeneitylayer-wise consistency object texture color object co- occurrence segmentation prior Recursion y=(segmentation, object) Horse Grass

11  The hierarchical structure means that the energy for the graph can be computed recursively.  Energy for states (y’s) of the L+1 levels is the energy of L levels plus energy terms linking level L to L+1. 11

12  Inference task:  Recursive Optimization: 12 Recursion  Polynomial-time Complexity:

13  Specify factor functions g(.) and f(.)  Learn their parameters from training data (supervised).  Structure Perceptron -- a machine learning approximation to Maximum Likelihood of parameters of P(W|I). 13

14 Input: a set of images with ground truth . Set parameters  Training algorithm (Collins 02): Loop over training samples: i = 1 to N Step 1: find the best using inference: Step 2: Update the parameters: End of Loop. 14 Inference is critical for learning

15  Task: Image Segmentation and Labeling.  Microsoft (and PASCAL) datasets. 15

16 16

17  MSRC – Global 81.2%, Average 74.1%  (state-of-art in CVPR 2008).  Note: with lowest level only (no hierarchy):  Global 75.9%, Average 67.2%.  Note: accuracy very high approx 95% for certain classes (sky, road, grass).  Pascal VOC 2007:  Global 67.2%, Average 26.5% (comparable to state-of-art).  Ladicky et al ICCV

18  Hierarchical Models of Objects.  Movable Parts.  Several Hierarchies to take into account different viewpoints.  Energy– data & prior terms.  Energy can be computed recursively.  Data partially supervised – object boxes.  Zhu, Chen, Torrabla, Freeman, Yuille (2010) 18

19 (1). Hierarchical part-based models with three layers. 4-6 models for each object to allow for pose. (2). Energy potential terms: (a) HOGs for edges, (b) Histogram of Words (HOWs) for regional appearance, (c) shape features. (3). Detect objects by scanning sub-windows using dynamic programming (to detect positions of the parts). (4). Learn the parameters of the models by machine learning: a variant (iCCCP) of Latent SVM.

20  Each hierarchy is a 3-layer tree.  Each node represents a part.  Total of 46 nodes:  ( x 9)  State variables -- each node has a spatial position.  Graph edges from parents to child – spatial constraints.

21  The parts can move relative to each other enabling spatial deformations.  Constraints on deformations are imposed by edges between parents and child (learnt). Parent-Child spatial constraints Parts: blue (1), yellow (9), purple (36) Deformations of the Horse Deformations of the Car

22  Each object is represented by 4 or 6 hierarchical models (mixture of models).  These mixture components account for pose/viewpoint changes.

23 The object model has variables: 1. p – represents the position of the parts. 2. V – specifies which mixture component (e.g. pose). 3. y – specifies whether the object is present or not. 4. w – model parameter (to be learnt). During learning the part positions p and the pose V are unknown – so they are latent variables and will be expressed as V=(h,p)

24 The “energy” of the model is defined to be: where is the image in the region.  The object is detected by solving:  If then we have detected the object.  If so, specifies the mixture component and the positions of the parts.

25  Three types of potential terms (1) Spatial terms specify the distribution on the positions of the parts. (2) Data terms for the edges of the object defined using HOG features. (3) Regional appearance data terms defined by histograms of words (HOWs – grey SIFT features and K-means).

26  Edge-like: Histogram of Oriented Gradients (Upper row)  Regional: Histogram Of Words (Bottom row)  HOGs HOWs

27  To detect an object requiring solving: for each image region.  We solve this by scanning over the sub- windows of the image, use dynamic programming to estimate the part positions and do exhaustive search over the

28  The input to learning is a set of labeled image regions.  Learning require us to estimate the parameters  While simultaneously estimating the hidden variables  Classically EM – approximate by machine learning, latent SVMs.

29  We use Yu and Joachim’s (2009) formulation of latent SVM.  This specifies a non-convex criterion to be minimized. This can be re-expressed in terms of a convex plus a concave part.

30  Following Yu and Joachims (2009) adapt the CCCP algorithm (Yuille and Rangarajan 2001) to minimize this criterion.  CCCP iterates between estimating the hidden variables and the parameters (like EM).  We propose a variant – incremental CCCP – which is faster.  Result: our method works well for learning the parameters without complex initialization.

31  Iterative Algorithm: Step 1: fill in the latent positions with best score(DP) Step 2: solve the structural SVM problem using partial negative training set (incrementally enlarge).  Initialization: No pretraining (no clustering). No displacement of all nodes (no deformation). Pose assignment: maximum overlapping  Simultaneous multi-layer learning

32  We use a quasi-linear kernel for the HOW features, linear kernels of the HOGs and for the spatial terms.  We use: (i) equal weights for HOGs and HOWs. (ii) equal weights for all nodes at all layers. (iii) same weights for all object categories.  Note: tuning weights for different categories will improve the performance.  The devil is in the details.

33  Post-processing: Rescoring the detection results  Context modeling: SVM+ contextual features best detection scores of 20 classes, locations, recognition scores of 20 classes  Recognition scores (Lazebnik CVPR06, Van de Sande PAMI 2010, Bosch CIVR07) SVM + spatial pyramid + HOWs (no latent position variable)





38  Mean Average Precision (mAP).  Compare AP’s for Pascal 2010 and Methods (trained on 2010) MIT- UCLA NLPRNUSUoCTTIUVAUCI Test on Test on

39  Brief sketch of compositional models with shared parts.  Motivation – scaling up to multiple objects/viewpoints/poses.  Efficient representation, learning, and inference.  Zhu, Chen, Lin, Lin, Yuille (2008, 2011).  Zhu, Chen, Torrabla, Freeman, Yuille (2010). 39

40  Objects and Images are constructed by compositions of parts – ANDs and ORs.  The probability models for are built by combining elementary models by composition.  Efficient Inference and Learning.

41 (1). Ability to transfer between contexts and generalize or extrapolate (e.g., from Cow to Yak). (2). Ability to reason about the system, intervene, do diagnostics. (3). Allows the system to answer many different questions based on the same underlying knowledge structure. (4). Scale up to multiple objects by part-sharing. “An embodiment of faith that the world is knowable, that one can tease things apart, comprehend them, and mentally recompose them at will.” “The world is compositional or God exists”.

42 42 Nodes of the Graph represents parts of the object. Parts can move and deform. y: (position, scale, orientation)

43  Introduce OR nodes and switch variables.  Settings of switch variables alters graph topology – allows different parts for different viewpoints/poses:  Mixtures of models – with shared parts. 43

44  Enables RCMs to deal with objects with multiple poses and viewpoints (~100).  Inference and Learning as before: 44

45  State of the art –  Zhu, Chen, Lin, Lin, Yuille CVPR 2008,

46 Strategy: share parts between different objects and viewpoints. 46

47  Unsupervised learning algorithm to learn parts shared between different objects.  Zhu, Chen, Freeman, Torrabla, Yuille  Structure Induction – learning the graph structures and learning the parameters.  Supplemented by supervised learning of masks. 47

48  120 templates: 5 viewpoints & 26 classes 48

49  Low-level to Mid-level to High-level.  Learn by suspicious coincidences. 49

50 50

51  Comparable to State of the Art. 51

52  Principle: Recursive Composition Composition -> complexity decomposition Recursion -> Universal rules (self-similarity) Recursion and Composition -> sparseness  A unified approach – object detection, recognition, parsing, matching, image labeling.  Statistical Models, Machine Learning, and Efficient Inference algorithms.  Extensible Models – easy to enhance.  Scaling up: shared parts, compostionality.  Trade-offs: sophistication of representation vrs. Features.  The Devil is in the Details. 52

53  Long Zhu, Yuanhao Chen, Antonio Torralba, William Freeman, AlanYuille. Part and Appearance Sharing: Recursive Compositional Models for Multi-View Multi-Object Detection. CVPR  Long Zhu, Yuanhao Chen, Alan Yuille, William Freeman. Latent Hierarchical Structural Learning for Object Detection. CVPR  Long Zhu, Yuanhao Chen, Yuan Lin, Chenxi Lin, Alan Yuille. Recursive Segmentation and Recognition Templates for 2D Parsing. NIPS  Long Zhu, Chenxi Lin, Haoda Huang, Yuanhao Chen, Alan Yuille. Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion. ECCV  Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan Yuille. Max Margin AND/OR Graph Learning for Parsing the Human Body. CVPR  Long Zhu, Yuanhao Chen, Xingyao Ye, Alan Yuille. Structure-Perceptron Learning of a Hierarchical Log-Linear Model. CVPR  Yuanhao Chen, Long Zhu, Chenxi Lin, Alan Yuille, Hongjiang Zhang. Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing. NIPS  Long Zhu, Alan L. Yuille. A Hierarchical Compositional System for Rapid Object Detection. NIPS

54 54 Composition Clustering Suspicious Coincidence Competitive Exclusion

55  Task: given 10 training images, n o labeling, no alignment, highly ambiguous features. Estimate Graph structure (nodes and edges) Estimate the parameters. 55 ? Combinatorial Explosion problem Correspondence is unknown

56  Unified representation (RCMs) and learning  Bridge the gap between the generic features and specific object structures 56

57 57 LevelCompositionClustersSuspicious Coincidence Competitive Exclusion Seconds ,43114, ,034,851741, ,135,4671,012, ,95572, More Sharing

58  What do the graph nodes represent?  Intuitively, receptive fields for parts of the horse.  From low-level  to high-level  Simple parts to  complex parts 58

59  Relate the parts to the image properties (e.g., edges) 59 * = [ Gabor, Edge, …]

60  Relate positions of parent parts to those of child parts. Triplets enable invariance to scale/angle. 60 (position, scale, orientation)

61  Fill in missing parts  Examine every node from top to bottom 61

62 62

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