The Visual Learning Group researches methods to acquire models of the real-world from visual data, such as images, videos, and motion sequences. Most of this research lies at the boundary between computer vision, machine learning and computer animation. Projects include learning image representations for visual recognition - the problem of determining what is where in a picture - but also extracting models of human movement from video and motion capture data.


  1. new paper to appear at ICCV 2015:
    High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object
    Features and its Applications to High-Level Vision
    G. Bertasius and J. Shi, and L. Torresani.

  1. new paper to appear at ICCV 2015:
    Learning Spatiotemporal Features with 3D Convolutional Networks,
    D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri.

  1. new article at CVPR 2015:
    DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection,
    G. Bertasius, J. Shi, and L. Torresani.

  1. new article in IEEE TPAMI (June 2015):
    What Can Pictures Tell Us About Web Pages? Improving Document Search using 
    S. Rodriguez-Vaamonde, L. Torresani, and A. Fitzgibbon.

  1. new article in IEEE TPAMI:
    Classemes and Other Classifier-based Features for Efficient Object Categorization,
    A. Bergamo, and L. Torresani.

  2. new article at WACV 2014:
    AutoCaption: Automatic Caption Generation for Personal Photos,
    K. Ramnath, S. Baker, L. Vanderwende, M. El-Saban, S.N. Sinha, A. Kannan,
    N. Hassan, M. Galley, Y. Yang, D. Ramanan, A. Bergamo, and L. Torresani.

  3. new article accepted at ICLR 2014:
    EXMOVES: Classifier-based Features for Scalable Action Recognition,
    D. Tran, and L. Torresani.

  1. L. Torresani wins a Neukom 2014 CompX grant.

  1. Thanks to Nvidia Corporation for supporting our research with a hardware donation.


We thank the following sources for supporting our research.

Logo design by Christine Claudino