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5 Top Computer Vision Trends in 2022

Computer vision is a field of artificial intelligence (AI) that helps computer systems understand the visual world and recognize objects within it.

Deep learning (DL) models are developed, which need to be trained to recognize faces, objects, and many different categories of digital images.

Computer vision has long suffered from severe limitations. It took a long time for technology to recognize simple differences, such as those between cats and dogs. However, a small child can easily do that.

Similarly, there are still many hurdles to overcome before self-driving cars become a reality. Despite having many cameras, sensors, and other technologies, they are known to run behind white vans in bright sunlight, because they fail to recognize that there is an obstacle in the their way. As such, technology is advancing rapidly.

Take a look below at some of the top trends that companies and IT teams are seeing in the computer vision market:

1. Synthetic Training

Early training of deep learning models is usually done using real examples. The process can be facilitated by using synthetic examples.

“Many computer vision models are trained on synthetic rather than real data,” said Steve Gu, co-founder and CEO, AiFi.

“Models trained 100% on synthetic and simulated scenes can now perform well in real settings without ever seeing a real example.”

This is driven by two key factors. On the one hand, advanced simulation tools and graphics processing units (GPUs) enable high-fidelity rendering in seconds and even milliseconds, making it possible to generate large amounts of quality training data with a high degree of accuracy. and details. In addition, there is increasing research on bridging the gap between the synthetic world and the real world, making it easier for models trained in simulation to adapt and generalize to real application scenarios.

“Instead of spending a lot of money on intensive human labor to annotate data, it’s time to investigate simulation tools and synthetic data for computer vision model training,” Gu said.

2. Holistic Perspective

Holger Kuehnle, executive creative director of ArtefactIt is noted that multiple networks of cameras, combined with sophisticated computer vision algorithms, are used to create holistic views of what is happening in an environment or a process – such as the floor of factory, for example.

Computer vision can be used – in combination with additional sensors, but also more alone – to create digital twins of environments or abstractions of what space is and what is happening in it, allowing the interesting automation scenarios.

For example, a camera can detect a spill on a factory floor and a cleaning robot can be automatically dispatched to clean up the spill. Currently, these scenarios are mainly driven by industrial automation, where many legacy production hardware are supplemented with sensors and cameras, so that they can comply with the level of data collection required for sophisticated AI and machine learning (ML) capabilities.

“Computer vision can sense when doors and windows are left open, when the washer or dryer is done, or when the stove is actually turned off,” Kuehnle said.

3. Self-Management

Computer vision technology often relies on a lot of human hand-holding. But it’s gradually evolved from that stage to the point where self-management is becoming more common, said Bruce King, data science technologist, Seagate Technology.

“Self-supervised machine learning methods applied to computer vision problems have greatly reduced the amount of expensive, annotated human images required to train models, ” said King.

“Self-supervised modeling technologies can reduce the cost of image annotation and enable more sophisticated models.”

4. Foundational Models

An outcrop of the self-management trend is the emergence of foundational models in computer vision.

King with Seagate noted that these foundational models are trained on large numbers of unlabeled images using self-directed methods.

These models are then adapted for a wide variety of computer vision tasks on a small number of training images.

5. Multiple Types of Data

The number of data types that can be used in computer vision is quite limited. That has been holding back the field for a long time.

Therefore, machine learning has been successfully applied to many types of data.

“Machine learning techniques in computer vision that combine images with their textual descriptions or captions facilitate exciting new capabilities, such as ML systems that generate new, unique images from text or reverse, generate captions and descriptions from images,” said King with Seagate.

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