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pro vyhledávání: '"A MCDONAGH"'
The generation and propagation sites of internal tides in the Mediterranean Sea are mapped through a comprehensive high-resolution numerical study. Two ocean general circulation models were used for this: NEMO v3.6, and ICON-O, both hydrostatic ocean
Externí odkaz:
http://arxiv.org/abs/2411.19790
The Segment Anything Model (SAM) was originally designed for label-agnostic mask generation. Does this model also possess inherent semantic understanding, of value to broader visual tasks? In this work we follow a multi-staged approach towards explor
Externí odkaz:
http://arxiv.org/abs/2411.15288
Visual domain gaps often impact object detection performance. Image-to-image translation can mitigate this effect, where contrastive approaches enable learning of the image-to-image mapping under unsupervised regimes. However, existing methods often
Externí odkaz:
http://arxiv.org/abs/2410.05058
Autor:
Xue, Yuyang, Yan, Junyu, Dutt, Raman, Haider, Fasih, Liu, Jingshuai, McDonagh, Steven, Tsaftaris, Sotirios A.
Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness in machine learning require a substantial amount of training data and
Externí odkaz:
http://arxiv.org/abs/2408.06890
Autor:
Ericsson, Linus, Espinosa, Miguel, Yang, Chenhongyi, Antoniou, Antreas, Storkey, Amos, Cohen, Shay B., McDonagh, Steven, Crowley, Elliot J.
Neural architecture search (NAS) finds high performing networks for a given task. Yet the results of NAS are fairly prosaic; they did not e.g. create a shift from convolutional structures to transformers. This is not least because the search spaces i
Externí odkaz:
http://arxiv.org/abs/2405.20838
Machine unlearning is a promising paradigm for removing unwanted data samples from a trained model, towards ensuring compliance with privacy regulations and limiting harmful biases. Although unlearning has been shown in, e.g., classification and reco
Externí odkaz:
http://arxiv.org/abs/2405.15517
Autor:
Tudosiu, Petru-Daniel, Yang, Yongxin, Zhang, Shifeng, Chen, Fei, McDonagh, Steven, Lampouras, Gerasimos, Iacobacci, Ignacio, Parisot, Sarah
Text-to-image generation has achieved astonishing results, yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed through cumbersome prompt engineering, scene layout conditioning, or
Externí odkaz:
http://arxiv.org/abs/2404.02790
Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high d
Externí odkaz:
http://arxiv.org/abs/2310.00986
Publikováno v:
Systematic Reviews, Vol 13, Iss 1, Pp 1-7 (2024)
Abstract Background A clinical quality registry (CQR) is a structured database that systematically collects data to monitor clinical quality and improve healthcare outcomes. The aims of CQRs are to improve treatment plans, assist in decision-making,
Externí odkaz:
https://doaj.org/article/16e9d1cb2e7f4ecf9de16843ac0d580b
Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted cl
Externí odkaz:
http://arxiv.org/abs/2304.01830