Papers Read on AI

Papers Read on AI header image 1
May 24, 2022  

Vision Transformer Adapter for Dense Predictions

May 24, 2022

This work investigates a simple yet powerful adapter for Vision Transformer (ViT). Unlike recent visual transformers that introduce vision-specific inductive biases into their architectures, ViT achieves inferior performance on dense prediction tasks due to lacking prior information of images. To solve this issue, we propose a Vision Transformer Adapter (ViT-Adapter), which can remedy the defects of ViT and achieve comparable performance to vision-specific models by introducing inductive biases via an additional architecture.

2022: Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, Y. Qiao

Ranked #1 on Semantic Segmentation on ADE20K val