Results 1 to 10 of about 5,361,838 (392)
Rethinking the Inception Architecture for Computer Vision [PDF]
Computer Vision and Pattern Recognition, 2015 Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks.
Ioffe, Sergey +4 more
core +4 more sources
Synchroinfo Journal, 2022 This article explores the subject of computer vision systems – a technology that allows vehicles to identify, track, and also classify objects on the roadway.
V. V. Mamrega
semanticscholar +2 more sources
The field of computer vision is surveyed and assessed, key research issues are identified, and possibilities for a future vision system are discussed. The problems of descriptions of two and three dimensional worlds are discussed.
Cunningham, R. +4 more
core +3 more sources
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [PDF]
IEEE International Conference on Computer Vision, 2021 This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such ...
Ze Liu +7 more
semanticscholar +1 more source
Masked Autoencoders Are Scalable Vision Learners [PDF]
Computer Vision and Pattern Recognition, 2021 This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs.
Kaiming He +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
Mixed Differential Privacy in Computer Vision [PDF]
Computer Vision and Pattern Recognition, 2022 We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data.
Aditya Golatkar +5 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
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development [PDF]
Proc. ACM Hum. Comput. Interact., 2021 Data is a crucial component of machine learning. The field is reliant on data to train, validate, and test models. With increased technical capabilities, machine learning research has boomed in both academic and industry settings, and one major focus has
M. Scheuerman, Emily L. Denton, A. Hanna
semanticscholar +1 more source