no code implementations • EAMT 2022 • Dimitar Shterionov, Mirella De Sisto, Vincent Vandeghinste, Aoife Brady, Mathieu De Coster, Lorraine Leeson, Josep Blat, Frankie Picron, Marcello Paolo Scipioni, Aditya Parikh, Louis ten Bosh, John O’Flaherty, Joni Dambre, Jorn Rijckaert
The SignON project (www. signon-project. eu) focuses on the research and development of a Sign Language (SL) translation mobile application and an open communications framework.
1 code implementation • 20 Jul 2023 • Maxim Bonnaerens, Joni Dambre
Vision transformers have demonstrated remarkable success in a wide range of computer vision tasks over the last years.
Ranked #4 on Efficient ViTs on ImageNet-1K (with DeiT-S)
no code implementations • 30 Jun 2023 • Mathieu De Coster, Ellen Rushe, Ruth Holmes, Anthony Ventresque, Joni Dambre
However, due to a domain mismatch with their training sets and challenging poses in sign language, they lack robustness on sign language data and image-based models often still outperform keypoint-based models.
no code implementations • 26 Aug 2022 • Maxim Bonnaerens, Matthias Freiberger, Marian Verhelst, Joni Dambre
In this work we propose a methodology to accurately evaluate and compare the performance of efficient neural network building blocks for computer vision in a hardware-aware manner.
no code implementations • 7 Feb 2022 • Mathieu De Coster, Dimitar Shterionov, Mieke Van Herreweghe, Joni Dambre
Automatic translation from signed to spoken languages is an interdisciplinary research domain, lying on the intersection of computer vision, machine translation and linguistics.
1 code implementation • International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL) 2021 • Mathieu De Coster, Karel D'Oosterlinck, Marija Pizurica, Paloma Rabaey, Severine Verlinden, Mieke Van Herreweghe, Joni Dambre
Our results show that pretrained language models can be used to improve sign language translation performance and that the self-attention patterns in BERT transfer in zero-shot to the encoder and decoder of sign language translation models.
1 code implementation • Computer Vision and Pattern Recognition Workshops (CVPRW) 2021 • Mathieu De Coster, Mieke Van Herreweghe, Joni Dambre
However, due to the limited amount of labeled data that is commonly available for training automatic sign (language) recognition, the VTN cannot reach its full potential in this domain.
Ranked #6 on Sign Language Recognition on AUTSL
1 code implementation • 1 Apr 2021 • Maxim Bonnaerens, Matthias Freiberger, Joni Dambre
In this work, we show that many anchors in the object detection head can be removed without any loss in accuracy.
1 code implementation • 31 Jan 2021 • Jules Talloen, Joni Dambre, Alexander Vandesompele
Using this framework, the potential of Hebbian learned feature extractors for image classification is illustrated.
1 code implementation • ICML Workshop LifelongML 2020 • Iryna Korshunova, Jonas Degrave, Joni Dambre, Arthur Gretton, Ferenc Huszar
One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control.
no code implementations • LREC 2020 • Mathieu De Coster, Mieke Van Herreweghe, Joni Dambre
Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition.
no code implementations • 9 Apr 2020 • Alexander Vandesompele, Gabriel Urbain, Francis wyffels, Joni Dambre
Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator.
no code implementations • 25 Oct 2019 • Matthias Freiberger, Peter Bienstman, Joni Dambre
Photonic reservoir computing is a promising candidate for low-energy computing at high bandwidths.
no code implementations • 6 Jun 2019 • Chonghuai Ma, Floris Laporte, Joni Dambre, Peter Bienstman
Using optical hardware for neuromorphic computing has become more and more popular recently due to its efficient high-speed data processing capabilities and low power consumption.
no code implementations • 8 Oct 2018 • Matthias Freiberger, Andrew Katumba, Peter Bienstman, Joni Dambre
As Moore's law comes to an end, neuromorphic approaches to computing are on the rise.
1 code implementation • EMNLP 2018 • Fréderic Godin, Kris Demuynck, Joni Dambre, Wesley De Neve, Thomas Demeester
In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations.
3 code implementations • NeurIPS 2018 • Iryna Korshunova, Jonas Degrave, Ferenc Huszár, Yarin Gal, Arthur Gretton, Joni Dambre
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.
2 code implementations • 25 Jul 2017 • Fréderic Godin, Jonas Degrave, Joni Dambre, Wesley De Neve
A DReLU, which comes with an unbounded positive and negative image, can be used as a drop-in replacement for a tanh activation function in the recurrent step of Quasi-Recurrent Neural Networks (QRNNs) (Bradbury et al. (2017)).
no code implementations • WS 2017 • Fréderic Godin, Joni Dambre, Wesley De Neve
In this paper, we introduce the novel concept of densely connected layers into recurrent neural networks.
no code implementations • ICCV 2017 • Iryna Korshunova, Wenzhe Shi, Joni Dambre, Lucas Theis
We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting.
no code implementations • 5 Nov 2016 • Jonas Degrave, Michiel Hermans, Joni Dambre, Francis wyffels
Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent.
1 code implementation • 5 Jun 2015 • Lionel Pigou, Aäron van den Oord, Sander Dieleman, Mieke Van Herreweghe, Joni Dambre
Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition.
Ranked #1 on Gesture Recognition on Montalbano
2 code implementations • 24 Mar 2015 • Sander Dieleman, Kyle W. Willett, Joni Dambre
Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images.
no code implementations • 12 Jan 2015 • Michiel Hermans, Miguel Soriano, Joni Dambre, Peter Bienstman, Ingo Fischer
We perform physical experiments that demonstrate that the obtained input encodings work well in reality, and we show that optimized systems perform significantly better than the common Reservoir Computing approach.
no code implementations • 24 Jul 2014 • Michiel Hermans, Michaël Burm, Joni Dambre, Peter Bienstman
Machine learning algorithms, and more in particular neural networks, arguably experience a revolution in terms of performance.
no code implementations • 9 Jun 2014 • Juan Pablo Carbajal, Joni Dambre, Michiel Hermans, Benjamin Schrauwen
In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing.