Machine vision is the technology that allows computer-based hardware and machines like robots and self-driving cars to perceive their environment using cameras and other optic sensors.
Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. ^Lee, Juho, et al. "Set transformer: A framework for attention-based permutation-invariant neural networks." International conference on machine learning. PMLR, 2019. ^Worrall, Daniel, and Max Welling. "Deep ...
A vision of augmented intelligence in which machine learning enables other processes rather than entirely replacing them is likely to have more impact. The development of a set of principles or guidelines by stakeholders can help shape best practices. Within...
Among such reviews, it is possible to find applications of ML and DL methodologies to a set of very heterogeneous domains including, to cite some, environment and energy applications [3,4,5], materials science [6,7], agriculture [8,9], natural language processing and computer vision [10,...
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 2921–2929. [Google Scholar] Selvaraju, R.R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. Grad-CAM: Visual Explanations from Deep Networks...
time. Embedded computing devices, cloud computing, 3D nonvisible multisensor fusion, artificial intelligence, and event-based imaging are some of the technologies enabling dynamic machine vision. Intelligent lighting is also critical since lighting in today’s applications must be adaptable and ...
AI finds application in diverse domains such as natural language processing, computer vision, robotics, and autonomous vehicles, driving innovation and transforming industries. Track 02: Machine Learning Machine learning is a subset of artificial intelligence where algorithms enable computers to learn from...
Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To...
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In this survey, we classify forgetting in machine learning into two categories: harmful forgetting and beneficial forgetting, based on the specific application scenarios. Harmful forgetting occurs when we desire the machine learning model to retain previously learned knowledge while adapting to new tasks...