Tag: Convolution Neural Network

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.

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.


A Brief Review of FlowNet

Recently, CNNs have been successfully used in estimating optical flow. Compared with traditional methods, these methods achieved a large improvement in quality. Here, we will give a brief review on the following papers.