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SEER: A Breakthrough in Self-Supervised Computer Vision Models?

Unite.AI

In the past decade, Artificial Intelligence (AI) and Machine Learning (ML) have seen tremendous progress. Modern AI and ML models can seamlessly and accurately recognize objects in images or video files. The SEER model by Facebook AI aims at maximizing the capabilities of self-supervised learning in the field of computer vision.

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How Northpower used computer vision with AWS to automate safety inspection risk assessments

AWS Machine Learning Blog

Specifically, we cover the computer vision and artificial intelligence (AI) techniques used to combine datasets into a list of prioritized tasks for field teams to investigate and mitigate. The workforce created a bounding box around stay wires and insulators and the output was subsequently used to train an ML model.

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Computer Vision Jobs that are Not Computer Vision Engineer

Viso.ai

As many areas of artificial intelligence (AI) have experienced exponential growth, computer vision is no exception. According to the data from the recruiting platforms – job listings that look for artificial intelligence or computer vision specialists doubled from 2021 to 2023.

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FeatUp: A Machine Learning Algorithm that Upgrades the Resolution of Deep Neural Networks for Improved Performance in Computer Vision Tasks

Marktechpost

Deep features are pivotal in computer vision studies, unlocking image semantics and empowering researchers to tackle various tasks, even in scenarios with minimal data. With their transformative potential, deep features continue to push the boundaries of what’s possible in computer vision.

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4 High-Value Applications of Computer Vision in Renewables

Viso.ai

Power Sector Priorities to Increase Renewable Energy Production – Source Computer vision methods have great potential for gathering useful data from digital images and videos. How is Computer Vision Used in Renewables? Thus, both energy providers and customers need better short-term production, demand, and forecasting.

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How Does Image Anonymization Impact Computer Vision Performance? Exploring Traditional vs. Realistic Anonymization Techniques

Marktechpost

However, when training computer vision models, anonymized data can impact accuracy due to losing vital information. The concern for individual privacy in visual data, especially in Autonomous Vehicle (AV) research, is paramount given the richness of privacy-sensitive information in such datasets.

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TensorFlow Lite – Real-Time Computer Vision on Edge Devices (2024)

Viso.ai

As an Edge AI implementation, TensorFlow Lite greatly reduces the barriers to introducing large-scale computer vision with on-device machine learning, making it possible to run machine learning everywhere. About us: At viso.ai, we power the most comprehensive computer vision platform Viso Suite. What is TensorFlow?