Zobrazeno 1 - 10
of 4 833
pro vyhledávání: '"P. McDonagh"'
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
Autor:
Bladen Melanie, McDonagh Janet, McLaughlin Paul, Gooding Richard, Holder Kerry-Ann, Thind Sharon, Klooster Brittany, Shields Alan, Turner-Bowker Diane M., Chatterton Kaitlin, Leso Allison, Volpi Connor, Sivasubramaniyam Sujan, Abulizi Jiawula, Khan Nisa
Publikováno v:
The Journal of Haemophilia Practice, Vol 11, Iss 1, Pp 108-122 (2024)
Early detection of joint bleeds is challenging yet critical for preserving joint health among individuals with haemophilia. This work explored early indicators of joint bleeds and young people with haemophilia B (YPwHB) self-monitoring practices to d
Externí odkaz:
https://doaj.org/article/2187d05f073840dc9d9acfcd1186f058
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
Behavior of neural networks is irremediably determined by the specific loss and data used during training. However it is often desirable to tune the model at inference time based on external factors such as preferences of the user or dynamic characte
Externí odkaz:
http://arxiv.org/abs/2304.00898
Autor:
McDonagh, James L., Wunsch, Benjamin H., Zavitsanou, Stamatia, Harrison, Alexander, Elmegreen, Bruce, Gifford, Stacey, van Kessel, Theodore, Cipcigan, Flaviu
The increasing importance of carbon capture technologies for deployment in remediating CO2 emissions, and thus the necessity to improve capture materials to allow scalability and efficiency, faces the challenge of materials development, which can req
Externí odkaz:
http://arxiv.org/abs/2303.14223