背景上,近年来基于Transformer和CNN的视觉基础模型取得巨大成功。有许多研究进一步地将Transformer结构与CNN架构结合,设计出了更为高效的hybrid CNN-Transformer Network。 这篇文章提出了一种名为"Scale-Aware Modulation Transformer"(SMT)的新型Transformer结构,它充分结合CNN和Transformer的优势,减轻了SA的运算负担,同时又...
有许多研究进一步地将Transformer结构与CNN架构结合,设计出了更为高效的hybrid CNN-Transformer Network。 这篇文章提出了一种名为"Scale-Aware Modulation Transformer"(SMT)的新型Transformer结构,它充分结合CNN和Transformer的优势,减轻了SA的运算负担,同时又解决了浅层的CNN局部特征捕捉能力的痛点。 在文章中,作者设计了...
作为本专栏的开篇,就让我们以近来的一篇被ICCV 2023接收的名为《Scale-aware Modulations Meet Transformer》的论文作为开端吧,一起踏上我们的视觉Backbone学习之旅吧。 图0 论文标题 二、研究动机 将Transformer应用到视觉任务,构建Vision Transformer(ViT)框架时,面临的最大痛点就是Transformer的核心机制:Self-attention(...
This repo is the official implementation of "Scale-Aware Modulation Meet Transformer". 📣 Announcement 18 Jul, 2023:The paper is available onarXiv. 16 Jul, 2023:The detection code and segmentation code are now open source and available!
& Jin, L. Scale-aware modulation meet transformer. In Proc. of the IEEE/CVF International Conference on Computer Vision, 6015–6026 (2023). Hatamizadeh, A. et al. Fastervit: Fast vision transformers with hierarchical attention. Preprint at arXiv:2306.06189 (2023). Ma, X. et al. Image ...
SMT-Net marries the Compact Axial Transformer Block with Scale-Adaptive Modulation, striking an effective balance between detection precision and computational expense. The Compact Axial Transformer Block comprises two innovative components: Compact Axial Attention and Fine-grained Feature Enhancement. Compact...
In this work, we describe a single-shot scale-aware convolutional neural network based face detector (SFDet). In comparison with the state-of-the-art anchor-based face detection methods, the main advantages of our method are summarized in four aspects. (1) We propose a scale-aware detection...
02TCSVT2021SwinNetSwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detectionresults, zf9s 03ICCV2021CMINetRGB-D Saliency Detection via Cascaded Mutual Information Minimizationresults, maav 04ICCV2021VSTVisual Saliency Transformerresults, rkq9 ...
The core module of a transformer is the self-attention block, which models relationships by calculating pairwise similarity between any two feature points. We believe the success of self-attention arises for two reasons: (i) self-attention captures long range dependencies, and (ii) the matrix mu...
其中attention前向传播代码如下,ca_attention的ca应该是cross-group information aggregation 的意思。看代码就是论文图中的SAM模块,即Scale-Aware Modulation。 先将特征图的通道分成num_heads组,此时形状为[num_heads,batch,C/num/heads,h,w],对每个组使用不同卷积核大小的深度卷积,并将结果cat起来,此时形状为[bat...