Zobrazeno 1 - 10
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pro vyhledávání: '"Audebert, A"'
Deep learning models have become a critical tool for analysis and classification of musical data. These models operate either on the audio signal, e.g. waveform or spectrogram, or on a symbolic representation, such as MIDI. In the latter, musical inf
Externí odkaz:
http://arxiv.org/abs/2407.17536
Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs), e.g. CLIP, for few-shot image classification. Despite their success, most prompt learning methods trade-off between classification accuracy and robustness, e.
Externí odkaz:
http://arxiv.org/abs/2407.01400
Autor:
Marsocci, Valerio, Audebert, Nicolas
Large-scale "foundation models" have gained traction as a way to leverage the vast amounts of unlabeled remote sensing data collected every day. However, due to the multiplicity of Earth Observation satellites, these models should learn "sensor agnos
Externí odkaz:
http://arxiv.org/abs/2405.09922
Publikováno v:
EARTHVISION 2024 IEEE/CVF CVPR Workshop. Large Scale Computer Vision for Remote Sensing Imagery, Jun 2024, Seattle, United States
Satellite imaging generally presents a trade-off between the frequency of acquisitions and the spatial resolution of the images. Super-resolution is often advanced as a way to get the best of both worlds. In this work, we investigate multi-image supe
Externí odkaz:
http://arxiv.org/abs/2404.16409
Autor:
Bellier, Georges Le, Audebert, Nicolas
Earth Observation imagery can capture rare and unusual events, such as disasters and major landscape changes, whose visual appearance contrasts with the usual observations. Deep models trained on common remote sensing data will output drastically dif
Externí odkaz:
http://arxiv.org/abs/2404.12667
Autor:
Ramzi, Elias, Audebert, Nicolas, Rambour, Clément, Araujo, André, Bitot, Xavier, Thome, Nicolas
In image retrieval, standard evaluation metrics rely on score ranking, \eg average precision (AP), recall at k (R@k), normalized discounted cumulative gain (NDCG). In this work we introduce a general framework for robust and decomposable rank losses
Externí odkaz:
http://arxiv.org/abs/2309.08250
Autor:
Juliane Herm, Hebun Erdur, Annette Aigner, Johannes Hengelbrock, Anselm Angermaier, Agnes Flöel, Annegret Hille, Claudia Gorski, Stephan Kinze, Ingo Schmehl, Gordian J. Hubert, Hanni Wiestler, Timo Siepmann, Martin Arndt, Christoph Gumbinger, Miriam Heyse, Joachim E. Weber, Heinrich J. Audebert, for the ANNOTeM-network
Publikováno v:
BMC Health Services Research, Vol 24, Iss 1, Pp 1-5 (2024)
Abstract Background Telemedicine provides specialized medical expertise in underserved areas where neurological expertise is frequently not available on a daily basis for hospitalized stroke patients. While tele-consultations are well established in
Externí odkaz:
https://doaj.org/article/5f49e30128504e3d81c55ff37e0e1531
Autor:
Leschiera, Emma, Al-Hity, Gheed, Flint, Melanie S., Venkataraman, Chandrasekhar, Lorenzi, Tommaso, Almeida, Luis, Audebert, Chloe
In recent in vitro experiments on co-culture between breast tumour spheroids and activated immune cells, it was observed that the introduction of the stress hormone cortisol resulted in a decreased immune cell infiltration into the spheroids. Moreove
Externí odkaz:
http://arxiv.org/abs/2307.12627
The latent space of GANs contains rich semantics reflecting the training data. Different methods propose to learn edits in latent space corresponding to semantic attributes, thus allowing to modify generated images. Most supervised methods rely on th
Externí odkaz:
http://arxiv.org/abs/2304.10508
Autor:
L. Brulin, S. Ducrocq, G. Even, M. P. Sanchez, S. Martel, S. Merlin, C. Audebert, P. Croiseau, J. Estellé
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Abstract Due to their potential impact on the host’s phenotype, organ-specific microbiotas are receiving increasing attention in several animal species, including cattle. Specifically, the vaginal microbiota of ruminants is attracting growing inter
Externí odkaz:
https://doaj.org/article/b7c964aed81a42e091ce04d91db93b78