Zobrazeno 1 - 10
of 2 065
pro vyhledávání: '"Fieguth, A."'
Autor:
Gharaee, Zahra, Lowe, Scott C., Gong, ZeMing, Arias, Pablo Millan, Pellegrino, Nicholas, Wang, Austin T., Haurum, Joakim Bruslund, Zarubiieva, Iuliia, Kari, Lila, Steinke, Dirk, Taylor, Graham W., Fieguth, Paul, Chang, Angel X.
As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, this paper presents the BIOSCAN-5M Insect dataset to the machine learning community and establish several benchmark tasks. BIOSCAN-5M is a comprehensive dataset con
Externí odkaz:
http://arxiv.org/abs/2406.12723
Machine comprehension of visual information from images and videos by neural networks faces two primary challenges. Firstly, there exists a computational and inference gap in connecting vision and language, making it difficult to accurately determine
Externí odkaz:
http://arxiv.org/abs/2403.03994
Single-image super-resolution (SISR) has seen significant advancements through the integration of deep learning. However, the substantial computational and memory requirements of existing methods often limit their practical application. This paper in
Externí odkaz:
http://arxiv.org/abs/2312.13396
Autor:
Spicker, Dylan, Nazemi, Amir, Hutchinson, Joy, Fieguth, Paul, Kirkpatrick, Sharon I., Wallace, Michael, Dodd, Kevin W.
Dietary intake data are routinely drawn upon to explore diet-health relationships. However, these data are often subject to measurement error, distorting the true relationships. Beyond measurement error, there are likely complex synergistic and somet
Externí odkaz:
http://arxiv.org/abs/2311.09338
Recent state-of-the-art semi-supervised Video Object Segmentation (VOS) methods have shown significant improvements in target object segmentation accuracy when information from preceding frames is used in segmenting the current frame. In particular,
Externí odkaz:
http://arxiv.org/abs/2309.15274
Autor:
Gharaee, Zahra, Gong, ZeMing, Pellegrino, Nicholas, Zarubiieva, Iuliia, Haurum, Joakim Bruslund, Lowe, Scott C., McKeown, Jaclyn T. A., Ho, Chris C. Y., McLeod, Joschka, Wei, Yi-Yun C, Agda, Jireh, Ratnasingham, Sujeevan, Steinke, Dirk, Chang, Angel X., Taylor, Graham W., Fieguth, Paul
In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is taxonomically classified by an expert, and also has associated genetic information including raw nu
Externí odkaz:
http://arxiv.org/abs/2307.10455
Autor:
Boktor, Marian, Tweel, James E. D., Ecclestone, Benjamin R., Ye, Jennifer Ai, Fieguth, Paul, Reza, Parsin Haji
Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in
Externí odkaz:
http://arxiv.org/abs/2307.01824
Autor:
Tweel, James E. D., Ecclestone, Benjamin R., Boktor, Marian, Simmons, James Alexander Tummon, Fieguth, Paul, Reza, Parsin Haji
Modern histopathology relies on the microscopic examination of thin tissue sections stained with histochemical techniques, typically using brightfield or fluorescence microscopy. However, the staining of samples can permanently alter their chemistry
Externí odkaz:
http://arxiv.org/abs/2306.08583
Unsupervised domain adaptation (UDA) aims to bridge the gap between source and target domains in the absence of target domain labels using two main techniques: input-level alignment (such as generative modeling and stylization) and feature-level alig
Externí odkaz:
http://arxiv.org/abs/2306.01706
Continual learning in real-world scenarios is a major challenge. A general continual learning model should have a constant memory size and no predefined task boundaries, as is the case in semi-supervised Video Object Segmentation (VOS), where continu
Externí odkaz:
http://arxiv.org/abs/2304.04259