AI Technology & Industry Review
A Google Brain research team explores the internal representation structures of ViTs and CNNs on image classification tasks, providing insights on key differences between the two approaches.
A research team from University of California San Diego and Microsoft proposes Micro-Factorized Convolution (MF-Conv), a novel approach that can deal with extremely low computational costs (4M–21M FLOPs) and achieves significant performance gains over state of the art models in the low FLOP regime.
A team from Google Research presents a neural video compression method based on generative adversarial networks (GANs) that outperforms previous neural video compression methods and advances generative video compression research.
A research team from Google Brain, Columbia University and University of Oxford proposes Graph Kernel Attention Transformers (GKATs), a new class of graph neural network that achieves greater expressive power than SOTA GNNs while reducing computation burdens.
A research team from Taichi Graphics, MIT CSAIL, Zhejiang University, Tsinghua University and Kuaishou Technology introduces a programming language and compiler for quantized simulation that achieves both high performance and significantly reduced memory costs by enabling flexible and aggressive quantization.
A research team from Cross Compass Ltd, Massachusetts Institute of Technology, Tokyo Institute of Technology and University of Tokyo presents CLIPDraw, an algorithm that synthesizes drawings based on natural language input without the need for any training.
A research team from Facebook AI and UC Berkeley finds a solution for vision transformers’ optimization instability problem by simply using a standard, lightweight convolutional stem for ViT models. The approach dramatically increases optimizer stability and improves peak performance without sacrificing computation efficiency.
A research team from Microsoft Research Asia, University of Science and Technology of China, Huazhong University of Science and Technology, and Tsinghua University takes advantage of the inherent spatiotemporal locality of videos to present a pure-transformer backbone architecture for video recognition that leads to a better speed-accuracy trade-off.
A research team from Google Cloud AI, Google Research and Rutgers University simplifies vision transformers’ complex design, proposing nested transformers (NesT) that simply stack basic transformer layers to process non-overlapping image blocks individually. The approach achieves superior ImageNet classification accuracy and improves model training efficiency.
A research team from Adobe proposes Directional GAN (DGAN), a novel and simple approach for generating high-resolution images conditioned on expected semantic attributes, greatly simplifying the image content generating process for marketing campaigns, websites and banners.
A research team from NYU and Facebook proposes MDETR, an end-to-end modulated detector that identifies objects in images conditioned on a raw text query and is able to capture a long tail of visual concepts expressed in free-form text.
A research team from Google Research propose a message-passing graph neural network that can explicitly model spatio-temporal relations, use either implicitly or explicitly representations of objects, and generalize previous structured models for video understanding.
A Google Research team accelerates Neural Radiance Fields’ rendering procedure for view-synthesis tasks, enabling it to work in real-time while retaining its ability to represent fine geometric details and convincing view-dependent effects.
Yann LeCun and a team of researchers propose Barlow Twins, a method that learns self-supervised representations through a joint embedding of distorted images, with an objective function that can make the embedding vectors almost identical while reducing redundancy between their components.
University of Oxford researchers propose COIN, a novel image compression method that stores the weights of an MLP overfitted to an image and outperforms JPEG at low bitrates even without entropy coding.
A team from Huawei, ISCAS and UCAS propose the novel Transformer-iN-Transformer (TNT) for modelling both patch-level and pixel-level representations.
A novel module that effectively and efficiently propagates information through an arbitrarily long video, with constant complexity w.r.t. number of frames and linear complexity w.r.t. resolution.
Researchers from UC Berkeley and Google Research have introduced BoTNet, a “conceptually simple yet powerful” backbone architecture that boosts performance on computer vision (CV) tasks such as image classification, object detection and instance segmentation.
A new study by University Tübingen introduces the world’s largest unified eye dataset with over 20 million human eye images captured using head-mounted eye trackers.
Researchers from Drexel University, Northeastern University and IT University Copenhagen explore how humans interact with AI in such contexts, with a focus on computer games.
In a new paper, researchers from Max Planck Institute for Informatics and Facebook Reality Labs propose an end-to-end trainable method that enables re-rendering of humans from one single image.
Recent AI research on speech separation has explored ways to associate lip motions in videos with audio, but this approach suffers when speakers’ lips are occluded, which they often are in busy multi-speaker environments.
VOGUE, an AI-powered optimization method that deforms garments according to a given body shape while preserving pattern and material details to deliver state-of-the-art photorealistic, high-resolution try-on images.
Researchers propose a novel framework and hierarchical predictive model that learns to identify what is predictable from unlabelled video.
The novel approach tackles dynamic 3D human-body synthesis from a sparse set of camera views, bettering existing methods on key metrics by significant margins.
Researchers proposed PGDrive, a driving simulator designed to evaluate and improve end-to-end driving agents’ generalization abilities.
Synced has invited Mr. Sheldon Fernandez to share his insights about the current development and future trends of artificial intelligence.
As part of our year-end series, Synced highlights 10 AI-powered art projects that inspired and entertained us in 2020.
Researchers combine the effectiveness of the inductive bias in CNNs with the expressivity of transformers to model and synthesize high resolution images.
In the new paper Canonical Capsules: Unsupervised Capsules in Canonical Pose, Turing Award Honoree Dr. Geoffrey Hinton and a team of researchers propose an architecture for unsupervised learning with 3D point clouds based on capsules.
University of Notre Dame and Facebook AI research propose Img2pose, real-time 6DoF 3D face pose estimation without face detection or landmark localization.
“Depix” is a new AI-powered tool that can easily undo pixelization to enable recovery of the information therein.
CSAIL researchers propose a framework for image reconstruction tasks using the state-of-the-art generative model StyleGAN2.
The new AI-powered Multi-Ingredient Pizza Generator (MPG) can deliver all these mouth-watering pies and many more.
NVIDIA blog introduced company’s latest NeurIPS presentation: applying a novel neural network training technique, adaptive discriminator augmentation, to the popular NVIDIA StyleGAN2 model.
The approach dramatically reduces bandwidth requirements by sending only a keypoint representation [of faces] and reconstructing the source video on the receiver side with the help of generative adversarial networks (GANs) to synthesize the talking heads.
A Princeton student designed a GAN framework for Chinese landscape painting generation that is so effective most humans can’t distinguish its works from the real thing.
Facebook’s new model enables free-viewpoint rendering of dynamic scenes in a single video.
Researchers from the City University of Hong Kong and SenseTime propose a lightweight matting objective decomposition network (MODNet) that can smoothly process real-time human matting from a single input image with diverse and dynamic backgrounds.
University of Alberta recently proposed U^2-Net, a novel deep network architecture that achieves very competitive performance in salient object detection.