Preface This paper systematically introduces the different categories of the Attention mechanism, and introduces the principles, advantages and disadvantages of each category.
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Overview
The purpose of the Attention mechanism is to focus on useful information and reduce the proportion of unimportant information. Attention mechanisms can be divided into 6 categories, including 4 basic categories and 2 combined categories. The four basic categories are channel attention, spatial attention, temporal attention, and branch attention. The two combination categories are the combination of channel and space, and the combination of space and time.
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A tutorial from TVM entry to practice
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TensorRT introductory to practice tutorial
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An introductory-to-practice tutorial for MNN
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Digital image processing and Opencv introductory to practice tutorial (must include digital image processing theory and opencv practice)
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OpenVINO entry-to-practice tutorial
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An introduction to libtorch tutorial
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Oneflow Getting Started to Practice Tutorial
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Detectron introductory to practice tutorial
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CUDA introductory to practice tutorial
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caffe source code reading
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pytorch source code reading
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Deep learning from entry to proficient (starting with convolutional neural networks)
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Interpretation of the latest summit. For example the recent CVPR2022 paper.
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Systematic review of various directions, development and evolution of main models, innovative ideas and advantages and disadvantages of each model, code analysis, etc. (Write it into multiple articles, it is not enough to write too simple, it is best to write an article with an important model)
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