Prof. Corso moved to the Electrical Engineering and Computer Science department at the University of Michigan in the 8/2014. He continues his work and research group in high-level computer vision at the intersection of perception, semantics/language, and robotics. Unless you are looking for something specific, historically, here, you probably would rather go to his new page.
Vision Seminar
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CSE 705: Vision Seminar on Spatiotemporal Video Analysis
SUNY at Buffalo
Fall 2010


Instructors: Jason Corso (jcorso), Raymond Fu (yunfu)
Course Webpage: http://www.cse.buffalo.edu/~jcorso/t/2010F_SEM
Meeting Times:M 11-1
Location: Vision Lab, Lockwood B20A
Office Hours: (Corso) M 4-5, F 2-3

News

  • First meeting is Monday, 8/30, to discuss paper list and logistics.

Main Course Material

Course Overview: This is a seminar course covering spatiotemporal video analysis. We will read and discuss papers on this topic throughout the semester, with the students primarily in charge of leading the discussions.

Prerequisites: It is assumed that the students have significance experience with computer vision, machine learning, and image analysis.

Grading: Grading is P/F unless a student specifically request otherwise.


Course Outline

See the paper list below for the full paper citations. I just list the authors here.
Date11-12Speaker 1 12-1 Speaker 2
8/30Ifeoma talk.IfeomaIntroduction 
9/6No Meeting   
9/13Research TalkJeff  
9/20Grundman et al. CVPR 10SagarResearch TalkJason
9/27Fei Fei et al. BMVC 05KushalResearch TalkCaiming
10/4Laptev et al. CVPR 08DuyguNo Meeting 
10/11Badrinarayanan et al. CVPR 10UtkarshRav-Acha et al. CVPR 06Kevin
10/18Savarese et al. MVC 08XinResearch TalkGang
10/25Bai et al. SIGGRAPH 08AlbertSun et al. CVPR 2009Caiming
11/1Ross et al. NIPS 05AishwaryaCVPR Round-UpAll
11/8No MeetingNo Meeting 
11/10 CVPR DEADLINE
11/15 Bobick and Davis PAMI 2001Ananth Blank et al. ICCV 05Jeff
11/22 BREAK WEEK
11/29Zhou et al. NIPS 06KushalResearch TalkAvik
12/6Zhu and Mumford FTCGV 07AvikResearch TalkSagar
Paper List
The paper list was circulated in class. This is a partial list and can be augmented by participants.
  • Video Segmentation
    • S. Paris. Edge-preserving smoothing and mean-shift segmentation of video streams. In ECCV, 2008.
    • W. Brendel and S. Todorovic. Video object segmentation by tracking regions. In ICCV, 2009.
    • Y. Huang, Q. Liu, and D. Metaxas. Video object segmentation by hypergraph cut. In CVPR, 2009.
    • M. Grundmann, V. Kwatra, M. Han, I. Essa, Efficient Hierarchical Graph-Based Video Segmentation, cvpr 2010
  • Video Segmentation (Interactive)
    • B. Price, B. Morse, and S. Cohen. Livecut: Learning-based interactive video segmentation by evaluation of multiple propagated cues. In ICCV, 2009.
    • X. Bai, J. Wang, D. Simons, and G. Sapiro. Video snapcut: robust video object cutout using localized classifiers. ACM SIGGRAPH, 28, 2009.
  • Spatiotemporal Interest Points
    • I. Laptev and T. Lindeberg. Space-time Interest Points. ICCV 2003.
    • P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. VS-PETS 2005.
    • Y. Ke, R. Sukthankar, and M. Hebert. Efficient Visual Event Detection using Volumetric Features. ICCV 2005.
    • A. Oikonomopoulos, I. Patras, and M. Pantic. Spatiotemporal Salient Points for Visual Recognition of Human Actions. SMC-B 36(3):710-719. 2006.
    • I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, pages 1–8, Anchorage, Alaska, June 2008.
  • Activity/Motion Recognition/Learning
    • R. Polana and R. C. Nelson. Detecting activities. CVPR 1993
    • A. Madabhushi and J. K. Aggarwal. A bayesian approach to human activity recognition. In VS ’99: Workshop on Visual Surveillance, page 25, 1999.
    • A. F. Bobick and J. W. Davis. The recognition of human movement using temporal templates. IEEE PAMI, 23:257– 267, 2001.
    • C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In ICPR, pages 32–36, 2004.
    • M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri. Actions as Space-Time Shapes. ICCV 2005. (or PAMI Version)
    • A. Bissacco and S. Soatto. Classifying Human Dynamics Without Contact Forces. CVPR 2006.
    • T. T. Truyen, D. Q. Phung, S. Venkatesh and H. H. Bui. AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition. CVPR 2006.
    • A. Veeraraghavan, R. Chellappa and A.K. Roy-Chowdhury. The Function Space of an Activity. CVPR 2006.
    • J. C. Niebles and L. Fei-Fei. A hierarchical model of shape and appearance for human action classification. CVPR 2007.
    • E. Shechtman and M. Irani. Space-time behavior based correlation -- OR -- How to tell if two underlying motion fields are similar without computing them? PAMI 2007. 29(11):2045-2056.
    • H. Jiang and D. R. Martin. Finding actions using shape flows. ECCV 2008.
    • J. Sun, X. Wu, S. Yan, L.-F. Cheong, T.-S. Chua, and J. Li. Hierarchical spatio-temporal context modeling for action recognition. In CVPR, 2009.
    • R. Messing, C. Pal, and H. Kautz. Activity recognition using the velocity histories of tracked keypoints. ICCV 2009.
  • Unsupervised Action Analysis
    • J. C. Niebles, H. Wang. and L. Fei-Fei. Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. BMVC 2005.
    • G. Mori, H. Jiang, M. S. Drew, Y. Wang. Unsupervised Discovery of Action Classes. CVPR 2006.
    • S. Savarese, A. D. Pozo, J. C. Niebles, and L. Fei-Fei. Spatial-temporal correlations for unsupervised action classification. In Motion and Video Computing, 2008.
  • Video Summarization (only a few)
    • H.-W. Kang, Y. Matsushita, X. Tang and X.-Q. Chen. Space-Time Video Montage. CVPR 2006.
    • A. Rav-Acha, Y. Pritch and S. Peleg. Making a Long Video Short: Dynamic Video Synopsis. CVPR 2006.
  • Technical Background Papers
    • D. Zhou, J. Huang, and B. Sch"okopf. Learning with hypergraphs: Clustering, classification, and embedding. In NIPS’06
    • R. Zass and A. Shashua. Probabilistic graph and hypergraph matching. In CVPR 2008
    • D. Freedman and P. Kisilev. Fast mean shift by compact density representation. In CVPR 2009.
    • D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental Learning for Robust Visual Tracking. In NIPS 2005.

last updated: Sat Jun 21 07:38:45 2014; copyright jcorso