Computers "We talk their language."

Computer vision is a field of artificial intelligence (AI) that focuses on the development of algorithms and models that can interpret and understand visual data from the world. This includes image recognition, object detection, image segmentation, and more.

One of the key techniques used in computer vision is deep learning, a type of machine learning that uses artificial neural networks with many layers to learn and make predictions from visual data. It’s almost like a human perceives or observes a real-life scenario by looking at it. ?This has led to significant breakthroughs in image recognition and object detection, with deep learning-based models achieving near-human levels of accuracy.

And in order to achieve that almost human-level accuracy, it is extremely important to perceive, understand and interpret that visual data in real-time, such as on video streams. This is extremely important for a wide range of applications, including self-driving cars, surveillance systems, and augmented reality.

Overall, computer vision is a rapidly-evolving field with many exciting developments and applications. With the continued advancement of AI technologies and the growing amount of visual data being generated, computer vision will play a critical role and breakthrough in many industries and applications in the future

There have been several recent developments in the field of computer vision. Some of the most notable include

1.????Self-supervised learning: Researchers have been developing self-supervised learning methods, which can learn features from unlabelled data, this approach has been shown to be useful in a wide range of computer vision tasks, including image classification, object detection, and semantic segmentation.

2.????Generative models: Generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have been used to generate new images, videos and 3D models from given data, this can be used to create synthetic data for training computer vision models, or to generate new images and videos for creative applications.

3.????Video understanding: There has been significant progress in the field of video understanding, which involves analyzing and understanding the content of video streams in real time. Researchers have been developing models that can detect and track objects in videos, understand the actions and interactions of people and objects, and generate natural language descriptions of the video content.

4.????Light-weight models: There have been recent developments in creating light-weight models that can perform well on a variety of computer vision tasks but with less computation power and memory, this is particularly important for deploying computer vision models on devices with limited resources, such as smartphones and IoT devices.

Adversarial attacks and Defenses: With the increasing use of computer vision models in real-world applications, there has been growing concern about the vulnerability of these models to adversarial attacks, in which malicious actors try to mislead the model by making small, carefully crafted changes to the input data. Researchers have been developing methods to defend against these attacks and to make computer vision models more robust

In short now visual analytics is the new buzz in Science.