Search Results for author: Silvia L. Pintea

Found 18 papers, 13 papers with code

Deep Continuous Networks

1 code implementation2 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.

Image Classification

A step towards understanding why classification helps regression

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.

Age Estimation Classification +2

Is there progress in activity progress prediction?

1 code implementation10 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.

Equal Bits: Enforcing Equally Distributed Binary Network Weights

1 code implementation2 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.

Binarization Quantization

Frequency learning for structured CNN filters with Gaussian fractional derivatives

no code implementations12 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.

Resolution learning in deep convolutional networks using scale-space theory

1 code implementation7 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.

Deep Hough-Transform Line Priors

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.

Line Segment Detection

Top-Down Networks: A coarse-to-fine reimagination of CNNs

1 code implementation16 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.

Decision Making

Hand-tremor frequency estimation in videos

no code implementations10 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.

Using phase instead of optical flow for action recognition

1 code implementation10 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.

Action Recognition Motion Magnification +3

Recurrent knowledge distillation

no code implementations18 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.

Knowledge Distillation

Deja Vu: Motion Prediction in Static Images

1 code implementation19 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.

Action Recognition motion prediction +2

Asymmetric kernel in Gaussian Processes for learning target variance

no code implementations19 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.

Gaussian Processes Metric Learning +1

Video Acceleration Magnification

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.

Motion Magnification Optical Flow Estimation +1

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