Original Source Here
5 Computer Vision Trends for 2021
ML Engineer, Sayak Paul presents key trends in Computer Vision
Computer Vision is a fascinating field of Artificial Intelligence that has tons of value in the real-world. There’s a huge wave of billion-dollar computer vision startups coming and Forbes expects the computer vision market to reach USD 49 billion by 2022.
The main goal of computer vision is to give computers the ability to understand the world through sight, and to make decisions based on their understanding.
In application, this technology allows the automation and augmentation of human sight, creating a myriad of use cases.
If AI enables computers to think, computer vision enables them to see, observe and understand. — IBM
Use cases of computer vision ranges from transportation to retail.
A quintessential example for transportation is the company Tesla, which manufactures electric self-driving cars that rely solely on cameras powered by computer vision models.
You also see computer vision revolutionizing the retail space, such as the Amazon Go program, which introduces checkout-free shopping using smart sensors and computer vision systems, taking convenience to the next level.
Computer Vision clearly has a lot to offer in terms of contributing to practical applications. As practitioners, or even someone having fun with deep learning, it’s important to look at the newest progress in the field, and keep up with the latest trends.
Trends in Computer Vision
Note this article won’t cover everything in the talk, and will only serve as a summary/takeaway. You can find the slides for the talk here with similar content, but with helpful links related to the topics. The talk is also published on YouTube which has more elaborations.
The goal of this article will be similar to his talk, which is to help you:
- discover what might be more exciting to work on in the coming days.
- inspire your next project idea.
- get up to speed a few the cutting edge stuff happening in the field.
Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot