Marktechpost
SEPTEMBER 2, 2023
In the ever-evolving field of computer vision, a pressing concern is the imperative to ensure fairness. Meta AI researchers have charted a comprehensive roadmap in response to this multifaceted challenge. These disparities underscore the need to evaluate and mitigate bias in computer vision models thoroughly.
Marktechpost
SEPTEMBER 24, 2023
However, when training computer vision models, anonymized data can impact accuracy due to losing vital information. Researchers continuously seek methods to maintain data utility while ensuring privacy. In this work, the authors examined the effects of anonymization on computer vision models for autonomous vehicles.
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Unite.AI
DECEMBER 20, 2023
In particular, the instances of irreproducible findings, such as in a review of 62 studies diagnosing COVID-19 with AI , emphasize the necessity to reevaluate practices and highlight the significance of transparency. Multiple factors contribute to the reproducibility crisis in AI research.
Towards AI
AUGUST 6, 2023
Are you overwhelmed by the recent progress in machine learning and computer vision as a practitioner in academia or in the industry? Motivation Recent updates in machine learning (ML) and computer vision (CV) are a mouthful, from Stable Diffusion for generative artificial intelligence (AI) to Segment Anything as foundation models.
Marktechpost
DECEMBER 14, 2023
Many branches of biology, including ecology, evolutionary biology, and biodiversity, are increasingly turning to digital imagery and computer vision as research tools. The researchers have identified two main obstacles to creating a vision foundation model in biology.
Marktechpost
SEPTEMBER 6, 2023
To overcome this problem, researchers from Skoltech and other institutions have devised a new way to distinguish weighted goods at a supermarket. The researchers used computer vision to facilitate this process. To facilitate this research, the researchers collected different types of images.
Marktechpost
JULY 12, 2023
ResNet expands on this achievement by including identity mappings through shortcut connections, enabling the training of deep neural networks with good performance across various computer vision applications, including image classification, object identification, and semantic segmentation.
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