Recent self-supervised learning (SSL) models trained on human-like egocentric visual inputs substantially underperform on image recognition tasks compared to humans. These models train on raw, uniform visual inputs collected from head-mounted cameras. This is different from humans, as the anatomical structure of the retina and visual cortex relatively amplifies the central visual information, i.e. around humans' gaze location. This selective amplification in humans likely aids in forming object-centered visual representations. Here, we investigate whether focusing on central visual information boosts egocentric visual object learning. We simulate 5-months of egocentric visual experience using the large-scale Ego4D dataset and generate gaze locations with a human gaze prediction model. To account for the importance of central vision in humans, we crop the visual area around the gaze location. Finally, we train a time-based SSL model on these modified inputs. Our experiments demonstrate that focusing on central vision leads to better object-centered representations. Our analysis shows that the SSL model leverages the temporal dynamics of the gaze movements to build stronger visual representations. Overall, our work marks a significant step toward bio-inspired learning of visual representations.

A new eye test that uses artificial intelligence AI to study retina scans can predict age-related macular degeneration (AMD) three years before symptoms start. The first part of the'pioneering' test, developed by researchers at University College London, is called DARC. DARC involves injecting dye into a person's bloodstream to illuminate'stressed' endothelial cells in the retina, so they appear bright white under a fluorescent camera. These'stressed' retinal cells could lead to abnormalities and later leaking blood vessels – causing AMD, which can severely compromise the central field of vision. The second part of the test uses an AI algorithm, trained to detect whether the highlighted white spots are around the macula – which indicates high AMD risk.

The light-sensitive layer found at the back of a person's eyes contains more than just cells that detect shadows and light -- it also contains information about the health of a person's entire body. And now, artificial intelligence can glean this information from a single snapshot, new research suggests. The new AI algorithm, which analyzes images of this light-sensitive layer of the eye, called the retina, could one day provide on the spot diagnoses of various ailments from diabetes to autoimmune and neurodegenerative diseases, the researchers claim. The AI algorithm was presented by Dr. Ursula Schmidt-Erfurth, the director of the ophthalmology department at the Medical University of Vienna, earlier this month at a scientific meeting in Vienna. Research on the algorithm was published Dec. 8 in the journal Ophthalmology.