Results 11 to 20 of about 5,349,509 (291)
IET Computer Vision, 2022 Despite recent advances in 3‐D pose estimation of human hands, thanks to the advent of convolutional neural networks (CNNs) and depth cameras, this task is still far from being solved in uncontrolled setups.
Meysam Madadi +3 more
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Deep learning-enabled medical computer vision
npj Digital Medicine, 2021 A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data.
A. Esteva +9 more
semanticscholar +1 more source
Advances in adversarial attacks and defenses in computer vision: A survey [PDF]
IEEE Access, 2021 Deep Learning is the most widely used tool in the contemporary field of computer vision. Its ability to accurately solve complex problems is employed in vision research to learn deep neural models for a variety of tasks, including security critical ...
Naveed Akhtar +3 more
semanticscholar +1 more source
The Programming Historian, 2022 This is the second of a two-part lesson introducing deep learning based computer vision methods for humanities research. This lesson digs deeper into the details of training a deep learning based computer vision model.
Daniel van Strien +4 more
doaj +1 more source
Vision Transformers in medical computer vision - A contemplative retrospection
Engineering applications of artificial intelligence, 2023 Vision Transformers (ViTs), with the magnificent potential to unravel the information contained within images, have evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision.
Arshi Parvaiz +5 more
semanticscholar +1 more source
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers [PDF]
Computer Vision and Pattern Recognition, 2022 Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate.
Dominik Zietlow +6 more
semanticscholar +1 more source
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (e.g., social network analysis and recommender systems), computer vision (e.g., object ...
Chaoqi Chen +7 more
semanticscholar +1 more source
Vision Transformers in Medical Computer Vision - A Contemplative Retrospection [PDF]
arXiv.org, 2022 Recent escalation in the field of computer vision underpins a huddle of algorithms with the magnificent potential to unravel the information contained within images.
Arshi Parvaiz +5 more
semanticscholar +1 more source
QUANTITATIVE COMPARISON BETWEEN NEURAL NETWORK- AND SGM-BASED STEREO MATCHING [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Over the last decades, various methods for three-dimensional detection of the environment have been developed and successfully used. This work considers classical stereo methods, which can determine depth information by the means of correspondence ...
A. Frenzel, N. Deckers, R. Reulke
doaj +1 more source
Sim-to-Real Transfer for Object Detection in Aerial Inspections of Transmission Towers
IEEE Access, 2023 Training deep learning models for object detection usually requires a large amount of data, a condition that is not common for most real-world applications, especially in the context of aerial imagery.
Augusto J. Peterlevitz +15 more
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