受到NLP领域的鼓舞,许多研究者尝试将Transformer引入图像分类领域,ViT[1]首先实现了和CNN类似的准确度,在一些backbone上,首先介绍 在图像分类上的原始的视觉Transformer,然后介绍Transformer增强CNN的方法,使用Transformer去增强CNNbackbone的long-range dependency(长范围的依赖)。Transformer在捕获全局信息有着很强的能力,但是...
原因就是ACL上的这篇带relative position encoding的Transformer, 妥当的相对位置编码可以带来卷积层天然具备...
1 简介 transformer 有助于学习 long-range depencencies,conv 有助于捕捉局部特征。结合这两点文章做出了三个改进:改进 tokenization 方式, ( image2token ); 改进 encoder network (Locally-enhanced Feed-Forward, Leff );在所有 transformer 层之后加了一层 layer-wise class token attention层,用来获得更好的全...
[TransT]Transformer Tracking (CVPR 2021) [LAMBDA NETWORKS]MODELING LONG-RANGE INTERACTIONS WITHOUT ATTENTION (ICLR) [UP-DETR]UP-DETR: Unsupervised Pre-training for Object Detection with Transformers (CVPR 2021) [VisTR]End-to-End Video Instance Segmentation with Transformers (CVPR 2021) Transformer Me...
将CNN提取low-level特征,强化局部特征提取的能力,与Transformer获取long-range信息的能力相结合提高模型性能。 Step1 : image-->tokens 利用卷积提取浅层特征信息 Vit将输入图像直接split成patch; CeiT利用conv+BN+Max-pooling提取浅层特征 Step 2 : 在空间维度上促进相邻token的相关性 ...
Incorporating Convolution Designs into Visual Transformers阅读笔记 Abstract 纯Transformer架构需要大量的训练数据或者额外的监督,才能获得与CNN相当的性能。为克服限制,提出一种新的Convolution-enhanced image Transformer(CeiT) 它结合了神经网络在提取低层特征、增强局部性方面的优势... ...
希望有大佬可以对比一下这篇论文和Graph-Based Global Reasoning Networks以及LatentGNN: Learning Efficient ...
Alternatively, we rethink this task from a convolution-free sequence-to-sequence perspective and predict saliency by modeling long-range dependencies, which can not be achieved by convolution. Specifically, we develop a novel unified model based on a pure transformer, namely, Visual Saliency ...
Self-attention is a pivotal mechanism within the Vision Transformer (ViT) model that enables it to capture relationships and dependencies between different patches in an image. It plays a crucial role in extracting contextual information and understanding long and short-range interactions among the p...
visual transformercomputational visual media(CVM)high-level visionlow-level visionimage generationmulti-modal learningTransformers,the dominant architecture for natural language processing,have also recently attracted much attention from computational visual media researchers due to their capacity for long-range ...