1 code implementation • 2 Feb 2024 • Nergis Tomen, Silvia L. Pintea, Jan C. van Gemert
CNNs and computational models of biological vision share some fundamental principles, which opened new avenues of research.
1 code implementation • ICCV 2023 • Silvia L. Pintea, Yancong Lin, Jouke Dijkstra, Jan C. van Gemert
A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss.
1 code implementation • 10 Aug 2023 • Frans de Boer, Jan C. van Gemert, Jouke Dijkstra, Silvia L. Pintea
We conclude that the progress prediction task is ill-posed on the currently used real-world datasets.
1 code implementation • ICCV 2023 • Xin Liu, Fatemeh Karimi Nejadasl, Jan C. van Gemert, Olaf Booij, Silvia L. Pintea
2) Improved efficiency by only doing the expensive feature computations on a small subset of all frames.
Ranked #1 on Video Object Detection on Waymo Open Dataset
1 code implementation • CVPR 2022 • Yancong Lin, Ruben Wiersma, Silvia L. Pintea, Klaus Hildebrandt, Elmar Eisemann, Jan C. van Gemert
Deep learning has improved vanishing point detection in images.
1 code implementation • 2 Dec 2021 • Yunqiang Li, Silvia L. Pintea, Jan C. van Gemert
We investigate experimentally that equal bit ratios are indeed preferable and show that our method leads to optimization benefits.
no code implementations • 12 Nov 2021 • Nikhil Saldanha, Silvia L. Pintea, Jan C. van Gemert, Nergis Tomen
Frequency information lies at the base of discriminating between textures, and therefore between different objects.
1 code implementation • 7 Jun 2021 • Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert
We use scale-space theory to obtain a self-similar parametrization of filters and make use of the N-Jet: a truncated Taylor series to approximate a filter by a learned combination of Gaussian derivative filters.
1 code implementation • CVPR 2021 • Xin Liu, Silvia L. Pintea, Fatemeh Karimi Nejadasl, Olaf Booij, Jan C. van Gemert
A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action.
1 code implementation • ECCV 2020 • Yancong Lin, Silvia L. Pintea, Jan C. van Gemert
Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features.
Ranked #3 on Line Segment Detection on wireframe dataset
1 code implementation • 16 Apr 2020 • Ioannis Lelekas, Nergis Tomen, Silvia L. Pintea, Jan C. van Gemert
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 preferential processing given relevant stimuli.
no code implementations • 10 Sep 2018 • Silvia L. Pintea, Jian Zheng, XiLin Li, Paulina J. M. Bank, Jacobus J. van Hilten, Jan C. van Gemert
We focus on the problem of estimating human hand-tremor frequency from input RGB video data.
1 code implementation • 10 Sep 2018 • Omar Hommos, Silvia L. Pintea, Pascal S. M. Mettes, Jan C. van Gemert
We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction.
no code implementations • 18 May 2018 • 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.
1 code implementation • 19 Mar 2018 • Silvia L. Pintea, Jan C. van Gemert, Arnold W. M. Smeulders
This paper proposes motion prediction in single still images by learning it from a set of videos.
no code implementations • 19 Mar 2018 • Silvia L. Pintea, Pascal S. Mettes, Jan C. van Gemert, Arnold W. M. Smeulders
This method introduces an efficient manner of learning action categories without the need of feature estimation.
no code implementations • 19 Mar 2018 • Silvia L. Pintea, Jan C. van Gemert, Arnold W. M. Smeulders
This enables each center to adjust the kernel space in its vicinity in correspondence with the topology of the targets --- a multi-modal approach.
1 code implementation • CVPR 2017 • Yichao Zhang, Silvia L. Pintea, Jan C. van Gemert
In these contexts there is often large motion present which severely distorts current video amplification methods that magnify change linearly.