2019 Turing Award Honours Computer Graphics Pioneers Hanrahan and Catmull
The Association for Computing Machinery (ACM) this morning announced Patrick M. (Pat) Hanrahan and Edwin E. (Ed) Catmull as its 2019 Turing Award winners.
AI Technology & Industry Review
The Association for Computing Machinery (ACM) this morning announced Patrick M. (Pat) Hanrahan and Edwin E. (Ed) Catmull as its 2019 Turing Award winners.
A research team has proposed non-contrast thoracic chest CT scans as an effective tool for detecting, quantifying, and tracking COVID-19.
A new study suggests that VSR models could perform even better if they used additional available visual information.
The earliest evidence of China’s recorded history is found in the Shang dynasty (~1600 to 1046 BC), and this hasContinue Reading
The model outperforms existing methods in image manipulation and offers researchers a possible solution to the scarcity of paired datasets.
UC Berkeley and Adobe Research have introduced a “universal” detector that can distinguish real images from generated images regardless of what architectures and/or datasets were used for training.
Proposed by researchers from the Rutgers University and Samsung AI Center in the UK, CookGAN uses an attention-based ingredients-image association model to condition a generative neural network tasked with synthesizing meal images.
The KaoKore dataset includes 5552 RGB image files drawn from the 2018 Collection of Facial Expressions dataset of cropped face images from Japanese artworks.
The crowdsourcing produced 111.25 hours of video from 54 non-expert demonstrators to build “one of the largest, richest, and most diverse robot manipulation datasets ever collected using human creativity and dexterity.”
Fast and accurate diagnosis is critical on the front line, and now an AI-powered diagnostic assessment system is helping Hubei medical teams do just that.
The proposed system is capable of searching the continental United States at 1 -meter pixel resolution, corresponding to approximately 2 billion images, in around 0.1 seconds.
MonoLayout, a practical deep neural architecture that takes just a single image of a road scene as input and outputs an amodal scene layout in bird’s-eye view.
In a bid to raise awareness of the threats posed by climate change, the Mila team recently published a paper that uses GANs to generate images of how climate events may impact our environments — with a particular focus on floods.
Joseph Redmon, creator of the popular object detection algorithm YOLO, tweeted last week that he had ceased his computer vision research to avoid enabling potential misuse of the tech.
Researchers from Italy’s University of Pisa present a clear and engaging tutorial on the main concepts and building blocks involved in neural architectures for graphs.
Researchers have proposed a novel generator network specialized on the illustrations in children’s books.
Researchers have proposed a simple but powerful “SimCLR” framework for contrastive learning of visual representations.
The tool enables researchers to try, compare, and evaluate models to decide which work best on their datasets or for their research purposes.
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.