AI Technology & Industry Review
Google teamed up with researchers from Synthesis AI and Columbia University to introduce a deep learning approach called ClearGrasp as a first step to teaching machines how to “see” transparent materials.
Researchers from Google Brain and Carnegie Mellon University have released models trained with a semi-supervised learning method called “Noisy Student” that achieve 88.4 percent top-1 accuracy on ImageNet.
Researchers introduced semantic region-adaptive normalization (SEAN), a simple but effective building block for conditional Generative Adversarial Networks (cGAN).
In a bid to simplify 3D deep learning and improve processing performance and efficiency, Facebook recently introduced an open-source framework for 3D computer vision.
Inspired by the performance of attention mechanisms in NLP, researchers have explored the possibility of applying them to vision tasks.
Researchers from Beijing’s National Laboratory of Pattern Recognition (NLPR), SenseTime Research, and Nanyang Technological University have taken the tech one step further with a new framework that enables totally arbitrary audio-video translation.
AI systems are already helping farmers with soil analysis, planting, animal husbandry, water conservation and more.
A team of researchers from Institut de Robòtica i Informàtica Industrial and Harvard University recently introduced 3DPeople, a large-scale comprehensive dataset with specific geometric shapes of clothes that is suitable for many computer vision tasks involving clothed humans.
To enable both content creators and end users to seriously restyle their apps’ interfaces while maintaining content detail clarity essential to their usability, researchers from Stanford have proposed ImagineNet, a novel and powerful new tool for interface customisation.
A trio of AI detecting breast cancer papers from Google, NYU, and DeepHealth have triggered huge discussions. What are the breakthroughs? Is AI truly beating radiologists? Where exactly are we right now?
A research team from the Hong Kong University of Science and Technology and Harbin Engineering University has adopted facial recognition technology to analyze students’ emotions in the classroom through a visual analytics system called “EmotionCues.”
Fake images and videos are giving AI a black eye — but how can the machine learning community fight back?
The Fujitsu Laboratories and R&D Center behavioural analysis technology identifies suspicious activity by analysing complex combinations of human actions and movements — and does so with minimal training data.
FAIR as now open-sourced PySlowFast, along with a pretrained model library and a pledge to continue adding cutting-edge resources to the project.
Recently, a dataset with 64,000 pictures of cars appeared on GitHub, the work of data scientist Nicolas Gervais.
A new study from Peking University and Microsoft Research Asia proposes a novel two-phase framework, FaceShifter, that aims for high-fidelity and occlusion-aware face exchange.
A team of researchers from Mila and Google Brain believe simple pencil sketches could help AI models generalize to a better understanding of unseen images.
Although “Sudoku“ grid-based number puzzles are no match for today’s artificial intelligence systems, a novel approach to the challenge is trending on GitHub due to its practical integration of computer vision technologies.