Zobrazeno 1 - 10
of 2 564
pro vyhledávání: '"Macq, A."'
Autor:
Bary, Tim, Macq, Benoit
Transformer neural networks require a large amount of labeled data to train effectively. Such data is often scarce in electroencephalography, as annotations made by medical experts are costly. This is why self-supervised training, using unlabeled dat
Externí odkaz:
http://arxiv.org/abs/2410.07190
Autor:
Khoury, Karim El, Zanella, Maxime, Gérin, Benoît, Godelaine, Tiffanie, Macq, Benoît, Mahmoudi, Saïd, De Vleeschouwer, Christophe, Ayed, Ismail Ben
Vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification methods still involves dividing large images into patches and making indepe
Externí odkaz:
http://arxiv.org/abs/2409.00698
Autor:
Gérin, Benoît, Halin, Anaïs, Cioppa, Anthony, Henry, Maxim, Ghanem, Bernard, Macq, Benoît, De Vleeschouwer, Christophe, Van Droogenbroeck, Marc
In the era of the Internet of Things (IoT), objects connect through a dynamic network, empowered by technologies like 5G, enabling real-time data sharing. However, smart objects, notably autonomous vehicles, face challenges in critical local computat
Externí odkaz:
http://arxiv.org/abs/2404.17930
We present a scalable framework designed to craft efficient lightweight models for video object detection utilizing self-training and knowledge distillation techniques. We scrutinize methodologies for the ideal selection of training images from video
Externí odkaz:
http://arxiv.org/abs/2404.10411
Emergency response missions depend on the fast relay of visual information, a task to which unmanned aerial vehicles are well adapted. However, the effective use of unmanned aerial vehicles is often compromised by bandwidth limitations that impede fa
Externí odkaz:
http://arxiv.org/abs/2402.04673
Autor:
Wuyckens, S., Dasnoy, D., Janssens, G., Hamaide, V., Huet, M., Loÿen, E., de Hertaing, G. Rotsart, Macq, B., Sterpin, E., Lee, J. A., Souris, K., Deffet, S.
Introduction. Treatment planning systems (TPS) are an essential component for simulating and optimizing a radiation therapy treatment before administering it to the patient. It ensures that the tumor is well covered and the dose to the healthy tissue
Externí odkaz:
http://arxiv.org/abs/2303.00365
Autor:
Piérard, Sébastien, Cioppa, Anthony, Halin, Anaïs, Vandeghen, Renaud, Zanella, Maxime, Macq, Benoît, Mahmoudi, Saïd, Van Droogenbroeck, Marc
Various tasks encountered in real-world surveillance can be addressed by determining posteriors (e.g. by Bayesian inference or machine learning), based on which critical decisions must be taken. However, the surveillance domain (acquisition device, o
Externí odkaz:
http://arxiv.org/abs/2211.10119
Purpose: To improve target coverage and reduce the dose in the surrounding organs-at-risks (OARs), we developed an image-guided treatment method based on a precomputed library of treatment plans controlled and delivered in real-time. Methods: A libra
Externí odkaz:
http://arxiv.org/abs/2211.00389
Publikováno v:
Health Research Policy and Systems, Vol 22, Iss 1, Pp 1-14 (2024)
Abstract Background The assessment of primary care organizations is considered to be essential for improving care. However, the assessments’ acceptability to professionals poses a challenge. Developing assessment programmes in collaboration with th
Externí odkaz:
https://doaj.org/article/0e137e090be34293af5493b0ea8d8c72
JPEG-XS offers low complexity image compression for applications with constrained but reasonable bit-rate, and low latency. Our paper explores the deployment of JPEG-XS on lossy packet networks. To preserve low latency, Forward Error Correction (FEC)
Externí odkaz:
http://arxiv.org/abs/2207.04825