Sparse Convolutional Neural Networks

no code implementations • CVPR 2015 • Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, Marianna Pensky

Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity.

Image Classification object-detection +1

Feature-Independent Action Spotting Without Human Localization, Segmentation or Frame-wise Tracking

no code implementations • CVPR 2014 • Chuan Sun, Marshall Tappen, Hassan Foroosh

To extract their internal dynamics, we devised a novel Two-Phase Decomposition (TP-Decomp) of a tensor that generates very compact and discriminative representations that are robust to even heavily perturbed data.

Action Spotting Template Matching

Probabilistic Label Trees for Efficient Large Scale Image Classification

no code implementations • CVPR 2013 • Baoyuan Liu, Fereshteh Sadeghi, Marshall Tappen, Ohad Shamir, Ce Liu

Large-scale recognition problems with thousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows.

Classification General Classification +1