Skip to content Skip to sidebar Skip to footer
Only AI Stuff
Only AI Stuff

Computer vision

Close

Researchers from NYU suggest the I2M2 approach for multi-modal learning which can capture both dependencies within and between different modalities.

AI Shorts, Applications, Artificial Intelligence, Computer vision, Editors Pick, Staff, Tech News, Technology, UncategorizedJune 19, 202465Views0Likes0Comments
Researchers from New York University, Genentech, and CIFAR have proposed a new paradigm to address inconsistencies in supervised multi-modal learning referred to as Inter & Intra-Modality Modeling (I2M2). Multi-modal learning is a critical facet of machine learning, used in autonomous vehicles, healthcare, and robotics, among other fields, where data from different modalities is mapped to…

Read More

Researchers utilize shadows to create 3D scene models, incorporating objects that are normally obstructed from sight.

Artificial Intelligence, Autonomous vehicles, Computer science and technology, Computer vision, Imaging, Light, Machine learning, Media Lab, Research, School of Architecture and Planning, Sensors, UncategorizedJune 18, 202477Views0Likes0Comments
Researchers from MIT and Meta have developed a computational vision technique, named PlatoNeRF, that allows for creating vivid, accurate 3D models of a scene from a single camera view. The innovative technology uses the shadowing in a scene to determine what could lie within obstructed areas. By combining machine learning with LIDAR (Light Detection and…

Read More

Comprehending the visual comprehension of language models.

Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJune 18, 202470Views0Likes0Comments
Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory (CSAIL) has revealed that language models without image experience still understand the visual world. The team found that even without seeing images, language models could write image-rendering code that could generate detailed and complicated scenes. The knowledge that enabled this process came from the vast…

Read More

Scientists improve the peripheral vision capabilities in AI models.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 17, 202483Views0Likes0Comments
Researchers at Massachusetts Institute of Technology (MIT) have developed an image dataset to simulate peripheral vision in artificial intelligence (AI) models. This step is aimed at helping such models detect approaching dangers more effectively, or predict whether a human driver would take note of an incoming object. Peripheral vision in humans allows us to see…

Read More

Scientists improve side vision capabilities in AI modules.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 17, 202484Views0Likes0Comments
Researchers from MIT have developed an image dataset that simulates peripheral vision in machine learning models, improving their object detection capabilities. However, even with this modification, the AI models still fell short of human performance. The researchers discovered that size and visual clutter, factors that impact human performance, largely did not affect the AI's ability.…

Read More

Scientists improve the side vision capabilities in artificial intelligence models.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 16, 202471Views0Likes0Comments
Peripheral vision, most humans' mechanism to see objects not directly in their line of sight, although with less detail, does not exist in AI. However, researchers at MIT have made significant progress towards this by developing an image dataset to simulate peripheral vision in machine learning models. The research indicated that models trained with this…

Read More

Scientists improve sideline sight in AI prototypes.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 16, 202469Views0Likes0Comments
MIT researchers are replicating peripheral vision—a human's ability to detect objects outside their direct line of sight—in AI systems, which could enable these machines to more effectively identify imminent dangers or predict human behavior. By equipping machine learning models with an extensive image dataset to imitate peripheral vision, the team found these models were better…

Read More

Scientists improve sideline viewing capabilities in AI systems.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 15, 202469Views0Likes0Comments
Peripheral vision, the ability to see objects outside of our direct line of sight, has been simulated by researchers at MIT to be used with AI technology. Unlike human vision, AI lacks the capability to perceive peripherally. Enhancing AI with this ability could greatly enhance its proactivity in identifying threats, and could even predict if…

Read More

+60 12-462 2768
hello@goacademyai.com