3 citations · 3 across the 2 of their papers we have counts for
9 papers
No frame left behind: Full Video Action Recognition
Xin Liu, Silvia L. Pintea, Fatemeh Karimi Nejadasl +2
Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundre…
Deep Hough-Transform Line Priors
Yancong Lin, Silvia L. Pintea, Jan C. van Gemert
Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants.…
Top-Down Networks: A coarse-to-fine reimagination of CNNs
Ioannis Lelekas, Nergis Tomen, Silvia L. Pintea +1
Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and prefe…
Using phase instead of optical flow for action recognition
Omar Hommos, Silvia L. Pintea, Pascal S. M. Mettes +1
Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dyn…
Hand-tremor frequency estimation in videos
Silvia L. Pintea, Jian Zheng, Xilin Li +3
We focus on the problem of estimating human hand-tremor frequency from input RGB video data. Estimating tremors from video is important for non-invasive monitoring, analyzing and d…
Recurrent knowledge distillation
Silvia L. Pintea, Yue Liu, Jan C. van Gemert
Knowledge distillation compacts deep networks by letting a small student network learn from a large teacher network. The accuracy of knowledge distillation recently benefited from…