Part 2: part-based models by Rob Fergus (MIT)

Problem with bag-of-words Motivation slide. Clearly location has to help, but bag-of-words doesn’t have it… All have equal probability for bag-of-words methods Location information is important

Overview of section Representation Recognition Computational complexity Location Appearance Occlusion, Background clutter Recognition Demos

Representation

Model: Parts and Structure Globally different, but have portions in common

Representation Object as set of parts Model: Generative representation Model: Relative locations between parts Appearance of part Issues: How to model location How to represent appearance Sparse or dense (pixels or regions) How to handle occlusion/clutter Figure from [Fischler & Elschlager 73]

History of Parts and Structure approaches Fischler & Elschlager 1973 Yuille ‘91 Brunelli & Poggio ‘93 Lades, v.d. Malsburg et al. ‘93 Cootes, Lanitis, Taylor et al. ‘95 Amit & Geman ‘95, ‘99 Perona et al. ‘95, ‘96, ’98, ’00, ’03, ‘04, ‘05 Felzenszwalb & Huttenlocher ’00, ’04 Crandall & Huttenlocher ’05, ’06 Leibe & Schiele ’03, ’04 Many papers since 2000

Sparse representation + Computationally tractable (105 pixels  101 -- 102 parts) + Generative representation of class + Avoid modeling global variability + Success in specific object recognition - Throw away most image information - Parts need to be distinctive to separate from other classes

Region operators Local maxima of interest operator function Can give scale/orientation invariance Figures from [Kadir, Zisserman and Brady 04]

The correspondence problem Model with P parts Image with N possible assignments for each part Consider mapping to be 1-1 Key problem with approach & motivates design choices. Assume for the moment that we have a set of regions. NP combinations!!!

The correspondence problem 1 – 1 mapping Each part assigned to unique feature As opposed to: 1 – Many Bag of words approaches Sudderth, Torralba, Freeman ’05 Loeff, Sorokin, Arora and Forsyth ‘05 Many – 1 - Quattoni, Collins and Darrell, 04

Location

Connectivity of parts Complexity is given by size of maximal clique in graph Consider a 3 part model Each part has set of N possible locations in image Location of parts 2 & 3 is independent, given location of L Each part has an appearance term, independent between parts. Shape Model Factor graph Variables L 2 3 L 2 3 Factors S(L) S(L,2) S(L,3) A(L) A(2) A(3) Shape Appearance

Different connectivity structures Felzenszwalb & Huttenlocher ‘00 Fergus et al. ’03 Fei-Fei et al. ‘03 Crandall et al. ‘05 Fergus et al. ’05 Crandall et al. ‘05 O(N2) O(N6) O(N2) O(N3) Csurka ’04 Vasconcelos ‘00 Bouchard & Triggs ‘05 Carneiro & Lowe ‘06 from Sparse Flexible Models of Local Features Gustavo Carneiro and David Lowe, ECCV 2006

How much does shape help? Crandall, Felzenszwalb, Huttenlocher CVPR’05 Shape variance increases with increasing model complexity Do get some benefit from shape

Hierarchical representations Pixels  Pixel groupings  Parts  Object Multi-scale approach increases number of low-level features Amit and Geman ‘98 Bouchard & Triggs ‘05 Images from [Amit98,Bouchard05]

Some class-specific graphs Articulated motion People Animals Special parameterisations Limb angles Images from [Kumar, Torr and Zisserman 05, Felzenszwalb & Huttenlocher 05]

Dense layout of parts Layout CRF: Winn & Shotton, CVPR ‘06 Part labels (color-coded) Each pixel is labelled – regions of pixels have the same part label. 18

How to model location? Explicit: Probability density functions Implicit: Voting scheme Invariance Translation Scaling Similarity/affine Viewpoint Affine transformation Similarity transformation Translation and Scaling Translation

Explicit shape model Cartesian Polar E.g. Gaussian distribution Parameters of model,  and  Independence corresponds to zeros in  Burl et al. ’96, Weber et al. ‘00, Fergus et al. ’03 Polar Convenient for invariance to rotation Mikolajczyk et al., CVPR ‘06

Spatial occurrence distributions Matched Codebook Entries Implicit shape model Use Hough space voting to find object Leibe and Schiele ’03,’05 Spatial occurrence distributions x y s Learning Learn appearance codebook Cluster over interest points on training images Learn spatial distributions Match codebook to training images Record matching positions on object Centroid is given Recognition Matched Codebook Entries Probabilistic Voting Interest Points

Deformable Template Matching Berg, Berg and Malik CVPR 2005 Template Query Formulate problem as Integer Quadratic Programming O(NP) in general Use approximations that allow P=50 and N=2550 in <2 secs % The idea of deformable templates is not new. In % the early 1970's at least three groups were % working on similar ideas: % Grenander in statistical pattern theory % von der Malsburg in neural networks % and Fischler and Eschlager in computer vision On the left we have the model template of a helicopter. We characterize the template by a set of feature points sampled from edges in the image. These are shown as the approximately 40 colored dots in the left image. On the right we show a query image. Here feature points are extracted along edges in the entire image. Many of these will be on background or clutter, but some are on the object of interest. Our problem is to find a correspondence between the model points at left and the correct subset of feature points on the right.

Other invariance methods Search over transformations Large space (# pixels x # scales ….) Closed form solution for translation and scale (Helmer and Lowe ’04) Features give information Characteristic scale Characteristic orientation (noisy) invariance of the characteristic scale Figures from Mikolajczyk & Schmid

Multiple views Mixture of 2-D models Frontal Profile Weber, Welling and Perona CVPR ‘00 20 40 60 80 100 50 55 65 70 75 85 90 95 Orientation Tuning angle in degrees % Correct Component 1 Component 2 Frontal Profile

Multiple view points Thomas, Ferrari, Leibe, Tuytelaars, Schiele, and L. Van Gool. Towards Multi-View Object Class Detection, CVPR 06 Hoiem, Rother, Winn, 3D LayoutCRF for Multi-View Object Class Recognition and Segmentation, CVPR ‘07

Appearance

Representation of appearance Needs to handle intra-class variation Task is no longer matching of descriptors Implicit variation (VQ to get discrete appearance) Explicit model of appearance (e.g. Gaussians in SIFT space) Dependency structure Often assume each part’s appearance is independent Common to assume independence with location

Representation of appearance Invariance needs to match that of shape model Insensitive to small shifts in translation/scale Compensate for jitter of features e.g. SIFT Illumination invariance Normalize out

Appearance representation SIFT Decision trees [Lepetit and Fua CVPR 2005] PCA Figure from Winn & Shotton, CVPR ‘06

Occlusion Explicit Implicit Additional match of each part to missing state Implicit Truncated minimum probability of appearance µpart Appearance space Log probability

Background clutter Explicit model Use a sub-window Generative model for clutter as well as foreground object Use a sub-window At correct position, no clutter is present

Recognition

What task? Classification Localization / Detection Object present/absent in image Background may be correlated with object Localization / Detection Localize object within the frame Bounding box or pixel-level segmentation

Efficient search methods Interpretation tree (Grimson ’87) Condition on assigned parts to give search regions for remaining ones Branch & bound, A*

Distance transforms Felzenszwalb and Huttenlocher ’00 & ’05 2 Model Felzenszwalb and Huttenlocher ’00 & ’05 Distance transforms O(N2P)  O(NP) for tree structured models How it works Assume location model is Gaussian (i.e. e-d2 ) Consider a two part model with µ=0, σ=1 on a 1-D image xi Image pixel Appearance log probability at xi for part 2 = A2(xi) Log probability f(d) = -d2

Distance transforms 2 For each position of landmark part, find best position for part 2 Finding most probable xi is equivalent finding maximum over set of offset parabolas Upper envelope computed in O(N) rather than obvious O(N2) via distance transform (see Felzenszwalb and Huttenlocher ’05). Add AL(x) to upper envelope (offset by µ) to get overall probability map xg xh xi xj xk xl Image pixel A2(xi) A2(xl) A2(xj) A2(xg) A2(xh) A2(xk) Log probability

Parts and Structure demo Gaussian location model – star configuration Translation invariant only Use 1st part as landmark Appearance model is template matching Manual training User identifies correspondence on training images Recognition Run template for each part over image Get local maxima  set of possible locations for each part Impose shape model - O(N2P) cost Score of each match is combination of shape model and template responses.

Demo images Sub-set of Caltech face dataset Caltech background images

Demo Web Page

Demo (2)

Demo (3)

Demo (4)

Demo: efficient methods

Stochastic Grammar of Images S.C. Zhu et al. and D. Mumford

Context and Hierarchy in a Probabilistic Image Model Jin & Geman (2006) animal head instantiated by bear head e.g. animals, trees, rocks e.g. contours, intermediate objects e.g. linelets, curvelets, T-junctions e.g. discontinuities, gradient animal head instantiated by tiger head

Parts and Structure models Summary Correspondence problem Efficient methods for large # parts and # positions in image Challenge to get representation with desired invariance Future directions: Multiple views Approaches to learning Multiple category training

References 2. Parts and Structure

[Agarwal02] S. Agarwal and D. Roth [Agarwal02] S. Agarwal and D. Roth. Learning a sparse representation for object detection. In Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, pages 113-130, 2002. [Agarwal_Dataset] Agarwal, S. and Awan, A. and Roth, D. UIUC Car dataset. http://l2r.cs.uiuc.edu/ ~cogcomp/Data/Car, 2002. [Amit98] Y. Amit and D. Geman. A computational model for visual selection. Neural Computation, 11(7):1691-1715, 1998. [Amit97] Y. Amit, D. Geman, and K. Wilder. Joint induction of shape features and tree classi- ers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(11):1300-1305, 1997. [Amores05] J. Amores, N. Sebe, and P. Radeva. Fast spatial pattern discovery integrating boosting with constellations of contextual discriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 2, pages 769-774, 2005. [Bar-Hillel05] A. Bar-Hillel, T. Hertz, and D. Weinshall. Object class recognition by boosting a part based model. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 1, pages 702-709, 2005. [Barnard03] K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. Jordan. Matching words and pictures. JMLR, 3:1107-1135, February 2003. [Berg05] A. Berg, T. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondence. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, volume 1, pages 26-33, June 2005. [Biederman87] I. Biederman. Recognition-by-components: A theory of human image understanding. Psychological Review, 94:115-147, 1987. [Biederman95] I. Biederman. An Invitation to Cognitive Science, Vol. 2: Visual Cognition, volume 2, chapter Visual Object Recognition, pages 121-165. MIT Press, 1995.

[Blei03] D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation [Blei03] D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, January 2003. [Borenstein02] E. Borenstein. and S. Ullman. Class-specic, top-down segmentation. In Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, pages 109-124, 2002. [Burl96] M. Burl and P. Perona. Recognition of planar object classes. In Proc. Computer Vision and Pattern Recognition, pages 223-230, 1996. [Burl96a] M. Burl, M. Weber, and P. Perona. A probabilistic approach to object recognition using local photometry and global geometry. In Proc. European Conference on Computer Vision, pages 628-641, 1996. [Burl98] M. Burl, M. Weber, and P. Perona. A probabilistic approach to object recognition using local photometry and global geometry. In Proceedings of the European Conference on Computer Vision, pages 628-641, 1998. [Burl95] M.C. Burl, T.K. Leung, and P. Perona. Face localization via shape statistics. In Int. Workshop on Automatic Face and Gesture Recognition, 1995. [Canny86] J. F. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698, 1986. [Crandall05] D. Crandall, P. Felzenszwalb, and D. Huttenlocher. Spatial priors for part-based recognition using statistical models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 1, pages 10-17, 2005. [Csurka04] G. Csurka, C. Bray, C. Dance, and L. Fan. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV, pages 1-22, 2004. [Dalal05] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, pages 886--893, 2005. [Dempster76] A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. JRSS B, 39:1-38, 1976. [Dorko04] G. Dorko and C. Schmid. Object class recognition using discriminative local features. IEEE Transactions on Pattern Analysis and Machine Intelligence, Review(Submitted), 2004.

[FeiFei03] L. Fei-Fei, R. Fergus, and P. Perona [FeiFei03] L. Fei-Fei, R. Fergus, and P. Perona. A Bayesian approach to unsupervised one-shot learning of object categories. In Proceedings of the 9th International Conference on Computer Vision, Nice, France, pages 1134-1141, October 2003. [FeiFei04] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In Workshop on Generative-Model Based Vision, 2004. [FeiFei05] L. Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, volume 2, pages 524-531, June 2005. [Felzenszwalb00] P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2066-2073, 2000. [Felzenszwalb05] P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. International Journal of Computer Vision, 61:55-79, January 2005. [Fergus_Datasets] R. Fergus and P. Perona. Caltech Object Category datasets. http://www.vision. caltech.edu/html-files/archive.html, 2003. [Fergus03] R. Fergus, P. Perona, and P. Zisserman. Object class recognition by unsupervised scaleinvariant learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 264-271, 2003. [Fergus04] R. Fergus, P. Perona, and A. Zisserman. A visual category lter for google images. In Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic, pages 242-256. Springer-Verlag, May 2004. [Fergus05 R. Fergus, P. Perona, and A. Zisserman. A sparse object category model for ecient learning and exhaustive recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 1, pages 380-387, 2005. [Fergus_Technote] R. Fergus, M. Weber, and P. Perona. Ecient methods for object recognition using the constellation model. Technical report, California Institute of Technology, 2001. [Fischler73] M.A. Fischler and R.A. Elschlager. The representation and matching of pictorial structures. IEEE Transactions on Computer, c-22(1):67-92, Jan. 1973.

[Grimson87] W. E. L. Grimson and T. Lozano-Perez [Grimson87] W. E. L. Grimson and T. Lozano-Perez. Localizing overlapping parts by searching the interpretation tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4):469-482, 1987. [Harris98] C. J. Harris and M. Stephens. A combined corner and edge detector. In Proceedings of the 4th Alvey Vision Conference, Manchester, pages 147-151, 1988. [Hart68] P.E. Hart, N.J. Nilsson, and B. Raphael. A formal basis for the determination of minimum cost paths. IEEE Transactions on SSC, 4:100-107, 1968. [Helmer04] S. Helmer and D. Lowe. Object recognition with many local features. In Workshop on Generative Model Based Vision 2004 (GMBV), Washington, D.C., July 2004. [Hofmann99] T. Hofmann. Probabilistic latent semantic indexing. In SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 15-19, 1999, Berkeley, CA, USA, pages 50-57. ACM, 1999. [Holub05] A. Holub and P. Perona. A discriminative framework for modeling object classes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 1, pages 664-671, 2005. [Kadir01] T. Kadir and M. Brady. Scale, saliency and image description. International Journal of Computer Vision, 45(2):83-105, 2001. [Kadir_Code] T. Kadir and M. Brady. Scale Scaliency Operator. http://www.robots.ox.ac.uk/ ~timork/salscale.html, 2003. [Kumar05] M. P. Kumar, P. H. S. Torr, and A. Zisserman. Obj cut. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, pages 18-25, 2005. [Leibe04] B. Leibe, A. Leonardis, and B. Schiele. Combined object categorization and segmentation with an implicit shape model. In Workshop on Statistical Learning in Computer Vision, ECCV, 2004. [Leung98] T. Leung and J. Malik. Contour continuity and region based image segmentation. In Proceedings of the 5th European Conference on Computer Vision, Freiburg, Germany, LNCS 1406, pages 544-559. Springer-Verlag, 1998. [Leung95] T.K. Leung, M.C. Burl, and P. Perona. Finding faces in cluttered scenes using random labeled graph matching. Proceedings of the 5th International Conference on Computer Vision, Boston, pages 637-644, June 1995.

[Leung98] T. K. Leung, M. C. Burl, and P. Perona [Leung98] T.K. Leung, M.C. Burl, and P. Perona. Probabilistic ane invariants for recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 678-684, 1998. [Lindeberg98] T. Lindeberg. Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2):77-116, 1998. [Lowe99] D. Lowe. Object recognition from local scale-invariant features. In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pages 1150-1157, September 1999. [Lowe01] D. Lowe. Local feature view clustering for 3D object recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, pages 682-688. Springer, December 2001. [Lowe04] D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004. [Mardia89] K.V. Mardia and I.L. Dryden. \Shape Distributions for Landmark Data". Advances in Applied Probability, 21:742-755, 1989. [Sivic05] J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman. Discovering object categories in image collections. Technical Report A. I. Memo 2005-005, Massachusetts Institute of Technology, 2005. [Sivic03] J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In Proceedings of the International Conference on Computer Vision, pages 1470-1477, October 2003. [Sudderth05] E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. Learning hierarchical models of scenes, objects, and parts. In Proceedings of the IEEE International Conference on Computer Vision, Beijing, page To appear, 2005. [Torralba04] A. Torralba, K. P. Murphy, and W. T. Freeman. Sharing features: ecient boosting procedures for multiclass object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, pages 762-769, 2004.

[Viola01] P. Viola and M. Jones [Viola01] P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 511{518, 2001. [Weber00] M.Weber. Unsupervised Learning of Models for Object Recognition. PhD thesis, California Institute of Technology, Pasadena, CA, 2000. [Weber00a] M. Weber, W. Einhauser, M. Welling, and P. Perona. Viewpoint-invariant learning and detection of human heads. In Proc. 4th IEEE Int. Conf. Autom. Face and Gesture Recog., FG2000, pages 20{27, March 2000. [Weber00b] M. Weber, M. Welling, and P. Perona. Towards automatic discovery of object categories. pages 2101{2108, June 2000. [Weber00c] M. Weber, M. Welling, and P. Perona. Unsupervised learning of models for recognition. In Proc. 6th Europ. Conf. Comp. Vis., ECCV2000, volume 1, pages 18{32, June 2000. [Welling05] M. Welling. An expectation maximization algorithm for inferring oset-normal shape distributions. In Tenth International Workshop on Articial Intelligence and Statistics, 2005. [Winn05] J. Winn and N. Joijic. Locus: Learning object classes with unsupervised segmentation. In Proceedings of the IEEE International Conference on Computer Vision, Beijing, page To appear, 2005

Quest for A Stochastic Grammar of Images Song-Chun Zhu and David Mumford

Example scheme Model shape using Gaussian distribution on location between parts Model appearance as pixel templates Represent image as collection of regions Extracted by template matching: normalized-cross correlation Manually trained model Click on training images

Connectivity of parts To find best match in image, we want most probable state of L, Run max-product message passing L 2 3 md ma mb mc S(L) S(L,2) S(L,3) A(L) A(2) A(3) Take O(N2) to compute: For each of the N values of L, need to find max over N states

Different graph structures 6 1 3 5 2 3 2 3 1 2 1 4 5 4 6 6 4 5 Fully connected Star structure Tree structure O(N6) O(N2) O(N2) Sparser graphs cannot capture all interactions between parts

Euclidean & Affine Shape Translation, rotation and scaling Euclidean Shape Removal of camera foreshortenings Affine Shape Assume Gaussian density in figure space What is the probability density for the shape variables in each of the different spaces? Feature space Translation Invariant shape Euclidean shape Affine shape Figures from [Leung98]

Translation-invariant shape Figure space density: Translation-invariant form e.g. P=3, move 1st part to origin Shape space density is still Gaussian

Affine Shape Density Affine Shape density (Dryden-Mardia): Euclidean Shape density is of similar form Can learnt parameters of DM density with EM! [Leung98],[Welling05]

Shape Shape is “what remains after differences due to translation, rotation, and scale have been factored out”. [Kendall84] Statistical theory of shape [Kendall, Bookstein, Mardia & Dryden] Y V X U Figure Space Shape Space Figures from [Leung98]

Learning

Learning situations Varying levels of supervision Unsupervised Image labels Object centroid/bounding box Segmented object Manual correspondence (typically sub-optimal) Generative models naturally incorporate labelling information (or lack of it) Discriminative schemes require labels for all data points Contains a motorbike

Learning using EM Task: Estimation of model parameters Chicken and Egg type problem, since we initially know neither: Model parameters - Assignment of regions to parts Let the assignments be a hidden variable and use EM algorithm to learn them and the model parameters

Learning procedure Find regions & their location & appearance Initialize model parameters Use EM and iterate to convergence: E-step: Compute assignments for which regions belong to which part M-step: Update model parameters Trying to maximize likelihood – consistency in shape & appearance

Example scheme, using EM for maximum likelihood learning 1. Current estimate of  2. Assign probabilities to constellations Large P ... pdf Image 1 Image 2 Image i Small P 3. Use probabilities as weights to re-estimate parameters. Example:  Large P x + Small P x + … = new estimate of 

Priors Implicit Explicit model () space 1 2 n Structure of dependencies in model Parameterisation of model Feature detectors Explicit p() MAP / Bayesian learning Fei-Fei ‘03 model () space 1 2 n p(n ) p(2 ) p(1 )

Learning Shape & Appearance simultaneously Fergus et al. ‘03

Learn appearance then shape Weber et al. ‘00 Model 1 Choice 1 Parameter Estimation Model 2 Choice 2 Parameter Estimation Preselected Parts (100) Predict / measure model performance (validation set or directly from model)

Discriminative training Sparse so parts need to be distinctive of class Boosted parts and structure models Amores et al. CVPR 2005 Bar Hillel et al. CVPR 2005 Discriminative features Weber et al. 2000 Ullman et al. Train discriminatively on parameters of generative model Holub, Welling, Perona ICCV 2005

Number of training images More supervision, fewer images needed Few unknown parameters Less supervision, more images. Lots of unknown parameters Over-fitting problems

Number of training examples 6 part Motorbike model Priors