.png){kind=icon}
返回
Fine-Grained Visual Computing Based on Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1145/3418215.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With increasing amounts of information, the image information received by people also increases exponentially. To perform fine-grained categorization and recognition of images and visual calculations, this study combines the Visual Geometry Group Network 16 model of convolutional neural networks and the vision attention mechanism to build amulti-level fine-grained image feature categorization model. Finally, the Tensor-Flow platform is utilized to simulate the fine-grained image classification model based on the visual attention mechanism. The results show that in terms of accuracy and required training time, the fine-grained image categorization effect of the multi-level feature categorization model constructed by this study is optimal, with an accuracy rate of 85.3% and a minimum training time of 108 s. In the similarity effect analysis, it is found that the chi-square distance between Log Gabor features and the degree of image distortion show a strong positive correlation; in addition, the validity of this measure is verified. Therefore, through the research in this study, it is found that the constructed fine-grained image categorization model has higher accuracy in image recognition categorization, shorter training time, and significantly better performance in similar feature effects, which provides an experimental reference for the visual computing of fine-grained images in the future.
Keyword:
Fine-grained
visual computing
visual attention mechanism
convolutional neural network
image classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.0K
被引数:
5.4K
机构
引用论文
Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition用于细粒度视觉识别的基于深度注意的空间递归网络
.png){kind=icon}
PREAI
分享收藏An Active Deep Learning Approach for Minimally Supervised PolSAR Image Classification一种用于最小监督PolSAR图像分类的主动深度学习方法
PREAI
分享收藏
Research watch: Coronavirus (COVID-19), mental health and social inclusion in the UK and Ireland
PREAI
分享收藏分享收藏
查看更多内容