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URI: http://hdl.handle.net/2144/1240

Recently Added

  • Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent 

    Orecchia, Lorenzo; Allen-Zhu, Zeyuan (2017)
    First-order methods play a central role in large-scale machine learning. Even though many variations exist, each suited to a particular problem, almost all such methods fundamentally rely on two types of algorithmic steps: ...
  • Automating image analysis by annotating landmarks with deep neural networks 

    Breslav, Mikhail; Hedrick, Tyson L.; Sclaroff, Stan; Betke, Margrit (2017)
    Image and video analysis is often a crucial step in the study of animal behavior and kinematics. Often these analyses require that the position of one or more animal landmarks are annotated (marked) in numerous images. The ...
  • Exploiting surroundedness for saliency detection: a boolean map approach 

    Zhang, Jianming; Sclaroff, Stan (2016-05-01)
    We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated ...
  • Scale and rotation invariant matching using linearly augmented trees 

    Jiang, Hao; Tian, Tai-Peng; Sclaroff, Stan (2015-12-01)
  • Do less and achieve more: Training CNNs for action recognition utilizing action images from the Web 

    Ma, Shugao; Bargal, Sarah Adel; Zhang, Jianming; Sigal, Leonid; Sclaroff, Stan (2017-08)
    Recently, attempts have been made to collect millions of videos to train Convolutional Neural Network (CNN) models for action recognition in videos. However, curating such large-scale video datasets requires immense human ...
  • Multidimensional scaling in the Poincare disk 

    Cvetkovski, Andrej; Crovella, Mark (2016-01-01)
    Multidimensional scaling (MDS) is a class of projective algorithms traditionally used in Euclidean space to produce twoor three-dimensional visualizations of datasets of multidimensional points or point distances. More ...
  • Salient object subitizing 

    Zhang, Jianming; Ma, Shugao; Sameki, Mehrnoosh; Sclaroff, Stan; Betke, Margrit; Lin, Zhe; Shen, Xiaohui; Price, Brian; Mech, Radomir (SPRINGER, 2017-09-01)
    We study the problem of salient object subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately ...
  • Top-down neural attention by excitation backprop 

    Zhang, Jianming; Lin, Zhe; Brandt, Jonathan; Shen, Xiaohui; Sclaroff, Stan; Bargal, Sarah Adel (Springer International Publishing, 2016)
    We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation ...
  • Unconstrained salient object detection via proposal subset optimization 

    Zhang, Jianming; Sclaroff, Stan; Lin, Zhe; Shen, Xiaohui; Price, Brian; Mech, Radomir (2016)
    We aim at detecting salient objects in unconstrained images. In unconstrained images, the number of salient objects (if any) varies from image to image, and is not given. We present a salient object detection system that ...
  • Personalizing gesture recognition using hierarchical bayesian neural networks 

    Joshi, Ajjen; Ghosh, Soumya; Betke, Margrit; Sclaroff, Stan; Pfister, Hanspeter (IEEE, 2017-01-01)
    Building robust classifiers trained on data susceptible to group or subject-specific variations is a challenging pattern recognition problem. We develop hierarchical Bayesian neural networks to capture subject-specific ...

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