"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
Welcome to SuperGradients, a free, open-source training library for PyTorch-based deep learning models. SuperGradients allows you to train or fine-tune SOTA pre-trained models for all the most commonly applied computer vision tasks with just one training library. We currently support object detection, image classification and semantic segmentation for videos and images. Easily load and fine-tune production-ready, pre-trained SOTA models that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy. Why do all the grind work, if we already did it for you?
Last year, UK-based robotics company Engineered Arts made a huge splash with an impressive demo of its humanoid robot with uber-uncanny expressions. Now, it's back -- and it's serving up an even wider range of some of the most realistic facial expressions we've ever seen. In a new video, the Ameca humanoid takes 12 new actuators for a spin as it stares into a mirror, contorting its face into expressions of disbelief, disgust, pain -- and even what can only be described as regret. In short, it's yet another fantastical demonstration of the power of cutting-edge robotics, and possibly even a sign of things to come. Engineered Arts also took the opportunity to take a swipe at Tesla's rather nebulous attempt to build a humanoid robot. In the video, an employee can be seen watching a video of the car company's infamous decision to use a dancer in a stretchy suit sub in for an actual humanoid robot.
Neuromorphic computing company GrAI Matter has $1 million in pre–orders for its GrAI VIP chip, the company told EE Times. The startup has engagement to date from companies across consumer Tier-1s, module makers (including ADLink, Framos, and ERM), U.S. and French government research, automotive Tier-1s and system integrators, white box suppliers, and distributors. As with previous generations of the company's Neuron Flow core, the company's approach for its GrAI VIP chip uses concepts from event–based sensing and sparsity to process image data efficiently. This means using a stateful neuron design (one that remembers the past) to process only information that has changed between one frame of a video and the next, which helps avoid processing unchanged parts of the frames over and over again. Combine this with a near–memory compute/dataflow architecture and the result is low–latency, low–power, real–time computer vision.
Continuing with our Object Detection release blog posts series, today, we'll showcase how to automate the training of the object detection models (and their predictions) that anyone will be able to create in BigML in short order. As discussed in previous posts, BigML already offers classification, regression, and unsupervised learning models (e.g., clustering, anomaly detection). They all accept images as just another input data type usable for model training. In fact, when images are uploaded a new Source is created for each and their corresponding IDs are added to a new Composite Source object with a new image field type. In summary, images can be combined with any other data type and can be assigned one or more labels by using the new label fields.
Ever scrolled through Instagram and spotted yourself in the background of a friend's photo looking a bit rough? A new AI-designed camera could fix the problem for you before it even happens -- though it's more intended as a solution for surveillance cameras than your pal's smartphone. Digital cameras are everywhere, and they see you a lot, thanks to facial recognition tech, body motion tracking, and medical imaging. These cameras pick up myriad details daily, resulting in massive troves of data and raising serious privacy concerns. To try and streamline what some of these lenses see, a group of researchers from the University of California, Los Angeles, has created cameras that can be taught to snap images of important objects while simultaneously erasing others from the shot.
Deep learning has been a game changer in the field of computer vision. It's widely used to teach computers to "see" and analyze the environment similarly to the way humans do. Its applications include self-driven cars, robotics, data analysis, and much more. In this blog post, we will explain in detail the applications of deep learning for computer vision. But before doing that, let's understand what computer vision and deep learning are. Computer vision (CV) is a field of artificial intelligence that enables computers to extract information from images, videos, and other visual sources. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images.
Mobile devices use facial recognition technology to help users quickly and securely unlock their phones, make a financial transaction or access medical records. But facial recognition technologies that employ a specific user-detection method are highly vulnerable to deepfake-based attacks that could lead to significant security concerns for users and applications, according to new research involving the Penn State College of Information Sciences and Technology. The researchers found that most application programming interfaces that use facial liveness verification--a feature of facial recognition technology that uses computer vision to confirm the presence of a live user--don't always detect digitally altered photos or videos of individuals made to look like a live version of someone else, also known as deepfakes. Applications that do use these detection measures are also significantly less effective at identifying deepfakes than what the app provider has claimed. "In recent years we have observed significant development of facial authentication and verification technologies, which have been deployed in many security-critical applications," said Ting Wang, associate professor of information sciences and technology and one principal investigator on the project.
A new presentation attack detection feature has been added to the Clearview Consent API from Clearview AI to allow developers to build spoof detection into identity verification solutions. Clearview Consent was launched just months ago to bring the company's facial recognition algorithms to a whole new set of use cases as a selfie biometrics tool, and the addition of presentation attack detection capabilities is the next step in its development, according to the people who made it. Clearview considered a range of approaches, and CEO Hoan Ton-That points out that developers do not typically have access to the specialized hardware behind device-based 3D biometric systems. Early engagement with Clearview Consent customers has yielded some insights into how businesses and developers plan to use it, which not only convinced the company to pursue liveness detection based on 2D images, but also imagine a range of applications. "We're looking at passive liveness video too, but some vendors have told us'We have these old profiles, and we want to find out how many of them are deepfakes and how many are presentation attacks," Clearview Ton-That tells Biometric Update in an interview.
Face biometrics are now firmly established as a way for people to unlock their mobile phones, or sign up to a new online account. As a core means of identifying a person, however, former UNDP Policy Advisor and Program Manager for Legal Identity Niall McCann thinks facial recognition may be on its way out. Biometrics are often part of the registration process, linking a person to their ID number, and ID documents may encode the individual's biometrics, number, or both. McCann tells Biometric Update's Frank Hersey in episode two that because facial recognition can be carried out without the consent or knowledge of the subject, unlike fingerprint biometrics, it is likely to be restricted by the UN for ID projects in the coming years. "You don't know when a CCTV camera system based on street corners is identifying you via facial recognition means," McCann explains.