vision transformer

by Synced 2022-04-07 1

Kaiming He’s MetaAI Team Proposes ViTDet: A Plain Vision Transformer Backbone Competitive With Hierarchical Backbones on Object Detection

A Meta AI research team explores the plain, non-hierarchical vision transformer (ViT) as a backbone network for object detection, proposing a ViT Detector that achieves performance competitive with traditional hierarchical backbones.

by Synced 2022-03-25 0

AI Machine Learning & Data Science Research

Microsoft’s FocalNets Replace ViTs’ Self-Attention With Focal Modulation to Improve Visual Modelling

A Microsoft Research team proposes FocalNet (Focal Modulation Network), a simple and attention-free architecture designed to replace transformers’ self-attention module. FocalNets exhibit significant superiority over self-attention for effective and efficient visual modelling in real-world applications.

by Synced 2022-01-13 1

AI Computer Vision & Graphics Machine Learning & Data Science Research

Facebook AI & UC Berkeley’s ConvNeXts Compete Favourably With SOTA Hierarchical ViTs on CV Benchmarks

A team from Facebook AI Research and UC Berkeley proposes ConvNeXts, a pure ConvNet model that achieves performance comparable with state-of-the-art hierarchical vision transformers on computer vision benchmarks while retaining the simplicity and efficiency of standard ConvNets.

by Synced 2021-12-16 1

AI Computer Vision & Graphics Machine Learning & Data Science Research

NVIDIA’s AdaViT Halts Token Computation to Adaptively Adjust ViT Inference Cost on Images of Different Complexity

Nvidia researchers propose AdaViT, an input-dependent mechanism that adaptively adjusts vision transformers’ inference cost by halting the compute of different tokens at different depths to reserve compute for discriminative tokens.

by Synced 2021-11-22 1

AI Computer Vision & Graphics Machine Learning & Data Science Research

Microsoft Asia’s Swin Transformer V2 Scales the Award-Winning ViT to 3 Billion Parameters and Achieves SOTA Performance on Vision Benchmarks

Microsoft Research Asia has upgraded their Swin Transformer with a new version featuring three billion parameters to train images with resolutions up to 1,536 x 1,536 and advance the SOTA on four representative vision benchmarks.

by Synced 2021-11-17 0

AI Machine Learning & Data Science Research

Is BERT the Future of Image Pretraining? ByteDance Team’s BERT-like Pretrained Vision Transformer iBOT Achieves New SOTAs

A research team from ByteDance, Johns Hopkins University, Shanghai Jiao Tong University and UC Santa Cruz seeks to apply the proven technique of masked language modelling to the training of better vision transformers, presenting iBOT (image BERT pretraining with Online Tokenizer), a self-supervised framework that performs masked prediction with an online tokenizer.

by Synced 2021-11-09 2

AI Machine Learning & Data Science Research

Can ViT Layers Express Convolutions? Peking U, UCLA & Microsoft Researchers Say ‘Yes’

In the new paper Can Vision Transformers Perform Convolution?, a research team from Peking University, UCLA and Microsoft Research proves that a single ViT layer with image patches as the input can perform any convolution operation constructively, and show that ViT performance in low data regimes can be significantly improved using their proposed ViT training pipeline.

by Synced 2021-08-27 4

AI Computer Vision & Graphics Machine Learning & Data Science Research

Google Brain Uncovers Representation Structure Differences Between CNNs and Vision Transformers

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.