Currently, there are applications based on Transformer in three major image problems:Classification (ViT), Detection (DETR) and Segmentation (SETR), all achieving good results. In the future, could Transformer possibly replace CNN? Will Transformer revolutionize the CV field just like its application in NLP? What might the research directions be? Please look forward to the next article … Read more
Author: Deng Yang This article is approximately 6300 words long and is recommended for a 10-minute read. This article briefly introduces the working principles of GAT based on the order discussed in the paper by Velickovic et al. (2017). When numbers are intangible, intuition is sparse; when forms are few, it is hard to delve … Read more
Author｜xys430381_1 https://blog.csdn.net/xys430381_1/article/details/89323444 This article is for academic sharing only. The copyright belongs to the author. If there is any infringement, please contact the backend for deletion. Table of Contents Overview Why Visual Attention is Needed Classification of Attention and Basic Concepts Soft Attention The application of two-level attention models in deep convolutional neural network for … Read more
Source: Python Data Science This article is about 7200 words, recommended reading time 14 minutes. In this article, we will explore the Transformer model and understand how it works. 1. Introduction The BERT model launched by Google achieved state-of-the-art results in 11 NLP tasks, triggering a revolution in the NLP field. One key factor for … Read more
来源：DeepHub IMBA This article is approximately 1800 words long and is recommended to be read in 5 minutes. This article will provide a detailed introduction to the principles and mechanisms of the masks in attention mechanisms. The attention mechanism mask allows us to send batches of data of varying lengths into the transformer at once. … Read more
Source: Deephub Imba This article is approximately 6500 words long and is recommended for a 10-minute read. This article will delve into how TurboAttention achieves efficiency improvements from a technical perspective and analyze its architectural innovations. As large language models (LLMs) continue to evolve in the AI application domain, their computational costs are also showing … Read more
Follow our official account to discover the beauty of CV technology This article shares the AAAI 2019 paper『AFS: An Attention-based mechanism for Supervised Feature Selection』, proposing a supervised feature selection mechanism based on the attention mechanism. Details are as follows: Paper link: https://arxiv.org/abs/1902.11074 Project link: https://github.com/upup123/AAAI-2019-AFS 01 Background and Introduction Feature selection is generally an … Read more
Click the above“Beginner Learning Vision” and select toStar or “Pin” Important information delivered first time Source｜Daniel Liu@Zhihu, https://zhuanlan.zhihu.com/p/138555896 Multi-scale self-guided attention for medical image segmentation The method in this paper is the optimal method for medical image segmentation on the CHAOS MRI Dataset published in 2019, with a final Dice score of 86.75. Introduction: Views … Read more
Click the “Beginner’s Guide to Vision” above, and choose to add “Star” or “Top“ Important content delivered at the first time Author｜xys430381_1 https://blog.csdn.net/xys430381_1/article/details/89323444 This article is for academic sharing only, copyright belongs to the author. If there is any infringement, please contact the background for deletion. Table of Contents Overview Why Visual Attention is Needed … Read more
Reprinted from: Machine Heart Lightning Attention-2 is a new type of linear attention mechanism that aligns the training and inference costs of long sequences with those of a 1K sequence length. The limitation of sequence length in large language models greatly restricts their applications in the field of artificial intelligence, such as multi-turn dialogue, long … Read more