Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Barnes, Nick"'
Publikováno v:
Machine Intelligence Research (2024)
Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach. We leverage the ability of masked autoencoders -- self-superv
Externí odkaz:
http://arxiv.org/abs/2306.07792
Deep convolutional neural networks perform better on images containing spatially invariant degradations, also known as synthetic degradations; however, their performance is limited on real-degraded photographs and requires multiple-stage network mode
Externí odkaz:
http://arxiv.org/abs/2004.13524
To make the best use of the underlying minute and subtle differences, fine-grained classifiers collect information about inter-class variations. The task is very challenging due to the small differences between the colors, viewpoint, and structure in
Externí odkaz:
http://arxiv.org/abs/2003.11154
As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations.
Externí odkaz:
http://arxiv.org/abs/1911.12885
Autor:
Anwar, Saeed, Barnes, Nick
Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neura
Externí odkaz:
http://arxiv.org/abs/1906.12021