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pro vyhledávání: '"Holder, Christopher J."'
A major challenge in machine learning is resilience to out-of-distribution data, that is data that exists outside of the distribution of a model's training data. Training is often performed using limited, carefully curated datasets and so when a mode
Externí odkaz:
http://arxiv.org/abs/2211.16228
Global localisation from visual data is a challenging problem applicable to many robotics domains. Prior works have shown that neural networks can be trained to map images of an environment to absolute camera pose within that environment, learning an
Externí odkaz:
http://arxiv.org/abs/2211.04718
Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for facilitating scene understanding and object detection. However, many of the top perfor
Externí odkaz:
http://arxiv.org/abs/2206.08605
Scene understanding for autonomous vehicles is a challenging computer vision task, with recent advances in convolutional neural networks (CNNs) achieving results that notably surpass prior traditional feature driven approaches. However, limited work
Externí odkaz:
http://arxiv.org/abs/1801.01235
Autor:
Holder, Christopher J.1 (AUTHOR), Ricketts, Stephen2 (AUTHOR), Obara, Boguslaw1 (AUTHOR) boguslaw.obara@durham.ac.uk
Publikováno v:
Machine Vision & Applications. Feb2020, Vol. 31 Issue 1/2, p1-13. 13p.
Global localisation from visual data is a challenging problem applicable to many robotics domains. Prior works have shown that neural networks can be trained to map images of an environment to absolute camera pose within that environment, learning an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec8bee0af2d74bd8ca6b7c838de9df44
Akademický článek
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Publikováno v:
Computer Vision - ECCV 2016 Workshops; 2016, p149-162, 14p