As seen in Figure 1, we have high-resolution processing near the center ofourgaze,i.e.,central and para-central regions, toidentify highly-detailed visual elements such as geometric shapes, and low-level details.

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks-- subtle, perceptually indistinguishable perturbations of inputs that change the response of the model. In the context of vision, we hypothesize that an important contributor to the robustness of human visual perception is constant exposure to low-fidelity visual stimuli in our peripheral vision. To investigate this hypothesis, we develop RBlur, an image transform that simulates the loss in fidelity of peripheral vision by blurring the image and reducing its color saturation based on the distance from a given fixation point. We show that compared to DNNs trained on the original images, DNNs trained on images transformed by RBlur are substantially more robust to adversarial attacks, as well as other, non-adversarial, corruptions, achieving up to 25% higher accuracy on perturbed data.

Why you'see' things in the dark, according to an ophthalmologist Science explains why we see flickers of light and patterns in the darkness. Our eyes sometimes really do play tricks on us at night. Breakthroughs, discoveries, and DIY tips sent every weekday. In 1999, Daniel Myrick and Eduardo Sánchez shot one of the definitive horror films of the era on a budget of roughly $60,000. is a study in omission, in the conspicuous absence of the visual effects characteristic to the genre. In lieu of baroque prosthetic gore and over-the-top CGI effects, the movie leans into silence and darkness for much of its 81-minute run time.

Human vision possesses a special type of visual processing systems called peripheral vision .

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks-- subtle, perceptually indistinguishable perturbations of inputs that change the response of the model. In the context of vision, we hypothesize that an important contributor to the robustness of human visual perception is constant exposure to low-fidelity visual stimuli in our peripheral vision. To investigate this hypothesis, we develop RBlur, an image transform that simulates the loss in fidelity of peripheral vision by blurring the image and reducing its color saturation based on the distance from a given fixation point. We show that compared to DNNs trained on the original images, DNNs trained on images transformed by RBlur are substantially more robust to adversarial attacks, as well as other, non-adversarial, corruptions, achieving up to 25% higher accuracy on perturbed data.

That was the average length of time an adult could focus on a screen for in 2021, according to research by Gloria Mark, a professor of informatics at the University of California. Twenty years ago, in 2004, that number stood at two-and-a-half minutes. Our attention spans – how long we're able to concentrate without being distracted – are shrinking. Our focus – how intensely we can think about things – is suffering too. The causes: technology that's designed to demand our attention; endless tools for procrastination at our fingertips; rising stress and anxiety disorders; and poor sleep quality.

We may earn revenue from the products available on this page and participate in affiliate programs. A solid 4K monitor provides all the screen real estate you need to get things done, whether working from home, gaming, or creating content. A high-resolution display will allow you to see text, photos, and video in great detail--especially recently created media. External monitors have been a common tech accessory for decades, but the marketplace for 4K displays specifically (like our best overall, the Dell S2722QC) has expanded greatly over the past few years. The combination of 4K PC and console games, accessible 4K video, and the necessity of a home office or learning space has accelerated this change. If you're assembling peripherals for a new computer setup, these are the best 4K monitors available right now. The monitor market has recently introduced several new 4K models, but these high-res options aren't the standard--if you want 4K resolution, that narrows the field right off the bat.

With the long-term goal of reducing the image processing time on an autonomous mobile robot in mind we explore in this paper the use of log-polar like image data with gaze control. The gaze control is not done on the Cartesian image but on the log-polar like image data. For this we start out from the classic deep reinforcement learning approach for Atari games. We extend an A3C deep RL approach with an LSTM network, and we learn the policy for playing three Atari games and a policy for gaze control. While the Atari games already use low-resolution images of 80 by 80 pixels, we are able to further reduce the amount of image pixels by a factor of 5 without losing any gaming performance.

Following the demise of smartphone-based headsets like Samsung's Gear VR and Google's Daydream, virtual reality headsets have generally fallen into two camps: lightweight standalone systems like the Quest 2 and more sophisticated PC-based systems like the Vive Pro 2 and Valve Index. But with the new Quest Pro, Meta is trying to combine the best things about both types of headsets into a powerful, but still very comfortable, self-contained unit. In fact, Meta believes so strongly in its next headset that prior to a demo session for press, Meta Product Management Lead Rupa Rao described the Quest Pro as "the beginning of an evolution in VR. It's going to be our first multi-functional immersive computing platform that will enable the metaverse." And after getting the chance to try it out myself, I can definitely see where that confidence is coming from.

New research from MIT suggests that a certain type of computer vision model that is trained to be robust to imperceptible noise added to image data encodes visual representations similarly to the way humans do using peripheral vision. Researchers find similarities between how some computer-vision systems process images and how humans see out of the corners of our eyes. Perhaps computer vision and human vision have more in common than meets the eye? Research from MIT suggests that a certain type of robust computer-vision model perceives visual representations similarly to the way humans do using peripheral vision. These models, known as adversarially robust models, are designed to overcome subtle bits of noise that have been added to image data.