论文名称:Vision Transformer Adapter for Dense Predictions 论文地址: 1 ViT-Adapter 论文解读: 1.1 背景和动机 近年来,Transformer 模型,得益于其动态建模的能力和长程依赖性,在计算机视觉领域取得了巨大的成功。使用 Vision Transformer 做下游任务的时候,用到的模型主要分为两大类:第1种是最朴素的直筒型 ViT[1...
最近的视觉Transformer引入了视觉特定的(Vision-Specific Transformer)归纳偏置(归纳性偏好),而ViT由于缺乏图像的先验信息,在密集预测任务中性能较差。为此,提出了一种ViT-Adapter,它可以弥补ViT的缺陷,…
摘要原文 This work investigates a simple yet powerful adapter for Vision Transformer(ViT). Unlike recent visual transformers that introduce vision-specificinductive biases into their architectures, ViT achieves inferior performance ondense prediction tasks due to lacking prior information of images. To solv...
Vision Transformers for Dense Prediction 论文链接:https://arxiv.org/abs/2103.13413v1论文代码:https://github.com/isl-org/DPT Abstract 本文引入dense vision transformers,它用vision transformers 代替卷积网络作为密集预测(dense prediction)任务的主干。将来自 Vision Transformer 各个阶段的token组装成各种分辨率的...
This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductive biases into their architectures, the plain ViT suffers inferior performance on dense predictions due to weak prior assumptions...
论文地址:[2102.12122] Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (arxiv.org) 代码地址:https://github.com/whai362/PVT 一、Motivation 1.将金字塔结构引入视觉Transformer,使视觉Transformer更适应密集预测性的任务; ...
CVPR2022 | MPViT: Multi-Path Vision Transformer for Dense Prediction 论文:https://arxiv.org/abs/2112.11010 代码:https://github.com/youngwanLEE/MPViT 主要内容 做了点啥 本文重点探究Transformer中的multi-scale patch embedding和multi-path structure scheme的设计。
DPT-Large、DPT-Base和DPT-Hybrid三种模型的不同之处在于ViT中重组连接层的设定，展示了Transformer在深度估计领域的早期尝试。尽管结构相对直观，但实验部分提到的“任意大小图片输入”并非独家创新，当时大部分ViT模型已经具备这种功能。作者通过分享个人经验，表达了对这一观点的反思和对文章期待的落空。实验...
论文标题:Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions发布于:ICCV 2021自己认为的关键词:ViT、Pyramid structure是否开源?:https://github.com/whai362/PVT2. 论文速览论文动机:现在的 ViT 主要用于图像分类任务,没有做密集预测任务的纯ViT 模型 ViT 的柱状结构(...
ViT-Adapter-L (single-scale, BEiTv2 pretrain, Mask2Former) PQ 58.4 # 6 Compare PQth65.0# 2 Compare PQst48.4# 6 Compare AP48.9# 7 Compare Object DetectionCOCO-OViT-Adapter (BEiTv2-L)Average mAP34.25# 11 Compare Effective Robustness7.79# 14 ...