1. Ullman, S. Structure and function in the early processing of visual information. In: Proceedings of the 3rd Annual Conference on Cognitive Science, 65-73, 1981

  2. Spoerri, A. and Ullman, S. The early detection of motion boundaries. In: Proceedings of the International Conference on Computer Vision, 209-219, June 1987.

  3. Dick, M. Ullman, S. and Sagi, D. Long range motion as a serial process. In: Proceedings of ARVO Meeting May 1987.

  4. Huttenlocher, D.P. and Ullman, S. Object recognition using alignment. In: Proceedings of the 1st International Conference on Computer Vision, London, 102-111, June 1987.

  5. Ullman, S. Computational theories of vision research. In: Proceedings of the NationalAcademy of Science: Frontiers of Visual Science, Washington, DC: NationalAcademy Press, 63-79, 1987.

  6. Shashua, A and Ullman, S. Structural 7. In: Proceedings of the International Conference on Computer Vision, Tampa, FL, 321-327, 1988.

  7. Basri, R and Ullman, S. The alignment of objects with smooth surfaces. In: Proceedings of the International Conference on Computer Vision, Tampa, FL, 482-488, 1988.

  8. Shoham, D and Ullman, S. Aligning a model to an image using minimal information. In: Proceeding of the International Conference on Computer Vision, Tampa, FL, 259-263, 198

  9. Ullman, S. The visual recognition of three-dimensional objects. In: O.E. Meyer and S. Kornblum (eds.), Attention and Performance XIV, Hillsdale, NJ: Lawrence Erlbaum Assoc., 1991.

  10. Shashua, A and Ullman, S. Grouping contours by iterated pairing network. In: Advances in Neural Information Processing, Proceedings of the Neural Information Processing Society 3 (Denver, CO, 1990), Santa Monica, CA: Morgan Kaufmann, 335-341, 1991.

  11. Ullman, S. Tacit assumptions in the computational study of vision. In: A. Gorea (ed.), Representations of Vision: Trends and Tacit Assumptions in Vision Research, Cambridge, England: CambridgeUniversity Press, 305-317, 1991. Hungarian translation, 1996.

  12. Ullman, S. Three-dimensional object recognition. In: Proceedings of the ColdSpringHarbor Symposium on Theoretical Biology: The Brain, Cold Spring Harbor, NY, 889-898, 1990.

  13. Shapira, Y. and Ullman, S. A pictorial approach to object classification. In: Proceedings of the 1991 International Joint Conference on Artificial Intelligence, Mountain View, CA: Morgan Kaufmann, 1991.

  14. Shoham, D. Ullman, S. and Grinvald, A. Characterization of dynamic patterns of cortical activity by a small number of principal components. In: Society of Neuroscience Abstracts, 17, 1991.

  15. Basri, R. and Ullman, S. A linear operator for object recognition. In: Neural Information System, Denver, Colorado, 452-459, 1991.

  16. Ullman, S. Models of image segmentation and object recognition. In: Proceedings of the Dahlem Conference: Exploring Brain Functions: Models in Neuroscience, (T.A.Poggio and D.A.Glaser, eds.), Berlin: John Wiley & Sons, 165-178, 1991.

  17. Bachelder, I.A. and Ullman, S. Contour matching using local affine transformations. CVPR, June 15-18, 1992, Champaign, IL.

  18. Guissin, A. and Ullman, S. Direct computation as the focus of expansion from velocity field measurements. Proceeding of Motion Conference, Princeton, NJ, 1991.

  19. Moses, Y. and Ullman, S. Limitations of non model-based recognition schemes. Proc. ECCV, Springer-Verlag, 820-828, 1992.

  20. Moses, Y. and Ullman, S. Non-negligible parameters for face recognition. Proc. Ninth Israeli Conf. on Art. Intel. and Vision, Dec. 1992.

  21. Bar, M. and Ullman, S. Spatial context in recognition. ARVO Abstract, 36(4), 5473, 1995.

  22. Kositsky, M. and Ullman, S. Learning class regions by the union of ellipsoids. Proc. of International Conference on Pattern Recognition (ICPR), Vienna, Austria, IEEE Computer Society Press, 750-757, 1996.

  23. Ullman, S. Aspects of segmentation and recognition. Proc. of the InternationalSchool of Biophysics, Ischia, Italy, 1996.

  24. Ullman, S and Zeira, A. Object recognition using stochastic optimization. Proceedings of the Venice Meeting on Optimization in Computer Vision. Springer-Verlag, 1997.

  25. Sali, E. and S. Ullman. Recognizing novel 3-D objects under new illumination and viewing position using a small number of example views or even a single view. Proceedings of the Sixth International Conference on Computer Vision, Bombay, India, 153-161, 1998.

  26. Brestel and Ullman, S. Multi-view modeling and synthesis. European Signal Processing Conference, Rhodes: 1293-1296, 1998.

  27. Sali, E. and Ullman, S. Combining class-specific fragments for object classification. Proceedings of the 10th British Machine Vision Conference Vol. 1, 203-213. Published by BVMA, 1999.

  28. Sali, E. and Ullman, S. Detecting object classes by the detection of overlapping 2-D fragments. In: Proceedings of the Workshop on Fundamental Structural Properties in Image and Pattern Analysis, 123-132, Published by OCG, Austrian Computer Society, 1999.

  29. Ullman, S. Sali, E. and Vidal-Naquet, M. A fragment-based approach to object representation and classification. In: A. Arcelli, L.P. Cordella and G. Sanniti di Baja (eds.), International Workshop on Visual Form, Berlin: Springer, 85-100, 2001.

  30. Borenstein, E. and S. Ullman, S. Class specific top down-segmentation. Proceedings of the European Conference on Computer Vision, 110-122, 2002.

  31. Vidal-Naquet, M. and Ullman, S. Object Recognition with Informative Features and Linear Classification. Proceedings of the 9th International Conference on Computer Vision, 281-288. Nice, France, 2003.

  32. Ullman, S. Approaches to visual recognition. In N. Kanwisher, J. Duncan (eds.), Attention and Performance XX, OxfordUniversity Press, 2003.

  33. Bart, E. Byvatov, E and Ullman, S. View-invariant recognition using corresponding object fragments. Proceedings ECCV, LNCS 3023, 152-165, Prague, 2004.

  34. Borenstein, E. and Ullman, S. Learning to segment. Proceedings ECCV, LNCS 3023, 315-328, Prague, 2004.

  35. Borenstein, E. and Ullman, S. Combining bottom-up and top-down segmentation. Proceedings of IEEE CVPR workshop on Perceptual Organization in Computer Vision, 2004.

  36. Bart, E. and Ullman, S. Class-based matching of object parts. Proceedings of IEEE CVPR Workshop on Image and Video Registration, 2004.

  37. Bart, E. and Ullman, S. Image normalization by mutual information. BMVC 2004.

  38. Bart, E. and Ullman, S. Learning a novel class from a single example by cross-generalization. Proceedings of IEEE CVPR 1063-1069, 2005.

  39. Ecker, A. and Ullman, S. A hierarchical non-parametric method for capturing non-rigid transformation. Proceedings of Canadian Robotics and Vision Conference, 50-56 2005.

  40. Bart, E. and Ullman, S. Single-example learning of novel classes using representation by similarity. British Machine Vision Conference , BMVC Oxford, England 2005.

  41. Epshtein, B. and Ullman, S. Identifying semantically equivalent object parts. Proceedings of IEEE CVPR 2-9, 2005.

  42. Epshtein, B. and Ullman, S. Hierarchical features for object classification. Proceedings IEEE ICCV, 220-227, 2005.

  43. Fink, M., Shalev-Shwartz, S., Singer, Y. and Ullman, S. Online Multiclass Learning by Interclass Hypothesis Sharing. Proceedings International Conference on Machine Learning ICML 313-320, 2006.

  44. Levi, D. and Ullman, S. Learning to classify by ongoing feature selection CRV; Candian Robotics and Vision Conference 2006. Recipient of Best CRV Paper Awawrd.

  45. Epstein, B. and Ullman, S. Satellite Features for the Classification of Visually Similar Classes. Proceedings IEEE CVPR 2079-2086, 2006.

  46. Bart, E and Ullman, S. Object recognition by eliminating distracting information. International Conference on Computer Vision and Graphics 2006 (Warsaw, Poland), 2006.

  47. Epshtein, B. and Ullman, S. Semantic Hierarchies for recognizing objects and parts, Proceedings CVPR, 1-8, 2007.

  48. Amit, Y., Srebro, N., Ullman, S. & Fink, M. Uncovering Shared Structures in Multiclass Classification. Int. Conf. Machine Learning, 227, p 17-24, 2007.

  49. Karlinsky,L. Dinershtein, M. Levi, D. Ullman, S. Unsupervised Classification and Part Localization by Consistency Amplification. IEEE ECCV 2, 321-335, 2008.

  50. Karlinsky,L. Dinershtein, M. Levi, D. Ullman, S. Combined model for detecting, localizing and recognizing faces. IEEE ECCV 2008 Workshop on Faces in Real-life Images, 1-14. 2009.

  51. Karlinsky, L, Dinerstein, M, Ullman, S. Unsupervised feature optimization (UFO): Simultaneous selection of multiple features with their detection parameters IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1263-1270, 2009

  52. Levi, D. & Ullman, S. 2009. Learning model complexity in an online environment. Proceedings of Canadian Robotics and Vision CVR, 260-267. IAPR Best Paper Award for 2009

  53. Karlinsky,L. Dinershtein, M. Harrari, D. Ullman, S. The chains model for detecting parts by their context. IEEE Computer Vision and Pattern Recognition (CVPR) 25-32, 2010.

  54. Karlinsky,L. Dinershtein, M., D. Ullman, S. Using body-anchored priors for identifying actions in single images. Neural Information Processing, 1-9, 2010.

  55. Karlinsky, L. Ullman, S. Using linking features in learning non-parametric part models. In: Springer-Verlag Berlin Heidelberg, A. Fitzgibbon et al. (Eds.): ECCV 327–340, 2012.

  56. 56. Harari, D and Ullman, S. Extending recognition in a changing environment. In: S. Battiato and J. Braz, (Eds.) Proceedings of International Conference on Computer Vision ICCV Theory and Applications, 2013.

  57. Dorfman, N., Harari, D and Ullman, S. Learning to perceive coherent objects. Marr Prize of Cog. Sci. Society, CogSci 2013, 394-399. 2013.

  58. Fetaya, E., Shamir, O. Ullman, S. Graph Approximation and Clustering on a Budget. Proceedings of 18th Int, Cinf, AI and Statistics, Vol. 18, 2015

  59. Fetaya, E. Ullman, S. Learning Local Invariant Mahalanobis Distances Proceedings of ICML, 2015

  60. Ben-Yossef, G., Ullman, S. 2015. A model for full local image interpretation. CogSci 220-225 2015

  61.  Berzak, Y. Barbu, A. Harari, D. Katz, B. Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic. Proc. Empirical Methods on Natural Language Processing, 2015

  62.  Rosenfeld, A. and Ullman, S. Hand-Object Interaction and Precise Localization in Transitive Action Recognition. Robot Vision (CRV) 2016.

  63. Rosenfeld, A. and Ullman, S. Visual Concept Recognition and Localization via Iterative Introspection. The 13th Asian Conference on Computer Vision (ACCV’16)

  64. Lifshitz, I., Ethan Fetaya, E., Ullman. S. Human Pose Estimation using Deep Consensus Voting. ECCV 2016

  65. Ben-Yosef, G. Yachin, A., Shimon Ullman, S. A model for interpreting social interactions in local image regions. AAAI Spring Symposium Series, Science of Intelligence, Palo Alto, CA, 2017.

  66. Ben-Yosef, G. and Ullman, S. 2017 Structured learning and detailed interpretation of minimal object images. ICCV Workshop on Mutual Benefits of Cognitive and Computer Vision (4 page summary)

  67. Altavini, TS, Astorga, G., Harari, D., Ullman, S., Reeke, G., Freiwald, W. and Gilbert, CD Object recognition in the Macaque based on informative components of familiar objects. SfN Abstract 2017

  68. Patish, U, Ullman, S. Cakewalk sampling arXiv:1802.09030, 2018