Machine Heart reports Machine Heart Editorial Team Researchers from the University of Rochester and Adobe Research have proposed a new generative network CM-GAN, which synthesizes overall structure and local details very well, significantly outperforming existing SOTA methods such as CoModGAN and LaMa in both quantitative and qualitative evaluations. Image inpainting refers to the process of … Read more
Click on the above “Beginner Learns Vision“, choose to add “Star Mark” or “Top“ Essential Insights Delivered Promptly Source | Intelligent Manufacturing Bureau Introduction Defect detection is a crucial aspect of the industrial production process, and the quality of its detection results directly affects product quality. In real-world scenarios, when the defect rate is very … Read more
Follow the WeChat public account “ML_NLP“ Set as “Starred“, to receive heavy content promptly! Reprinted from: AI Park Author: Sam Nolen Translation: ronghuaiyang Introduction Applicable in cases with very few samples. Even imperfect synthetic data can improve classifier performance. Generative Adversarial Networks (GANs) were introduced by Ian Goodfellow in 2014 and have become a very … Read more
CCF published a new issue of the “China Computer Science and Technology Development Report” in October 2018, detailing the research progress in ten directions, including the deep integration of AI and system software. We will share the highlights of the report in installments. Please join CCF and log in to the CCF digital library to … Read more
Follow our public account to discover the beauty of CV technology This article shares a recent literature review on super-resolution titled ‘Generative Adversarial Networks for Image Super-Resolution: A Survey’. It discusses the performance, advantages, disadvantages, complexity, challenges, and potential research points of 193 related papers. Specific information is as follows: Authors: Tian Chunwei, Zhang Xuan … Read more
Click the above “Beginner Learning Vision” to choose to add “Starred” or “Pinned“ Important content delivered at the first time The Essence of Convolution Conventional Convolution Single-channel Convolution Multi-channel Convolution 3D Convolution Transposed Convolution 1×1 Convolution Depthwise Separable Convolution Dilated Convolution The Essence of Convolution Before introducing various convolutions, it is necessary to revisit the … Read more
The American company Owl Autonomous Imaging’s Thermal Ranger system can locate and classify targets such as pedestrians in the dark using only a thermal infrared camera and a trained Convolutional Neural Network (CNN). Thermal imaging is particularly suitable for imaging after dark, as it relies on the infrared energy emitted by objects themselves rather than … Read more
“Academic Window” is a paper recommendation column launched by the Academic Research Department of the Graduate Affairs Center of the School of Computer Science and Technology, aimed at recommending and sharing the latest academic achievements and classic papers in various fields of computer science to students. In the future, it will be pushed on this … Read more
Click on the “Visual Learning for Beginners” above, choose to add “Star” or “Pin“. Essential Knowledge Delivered Instantly This article is sourced from the AI Knowledge Base and reprinted from Smart Vehicle Technology. The article is for academic exchange only. / Introduction/ Object tracking is an important problem in the field of computer vision, currently … Read more
Follow our public account to discover the beauty of CV technology 0 Introduction In Convolutional Neural Networks (CNN), convolution operations excel at extracting local features, but there are certain limitations in capturing global feature representations. In Vision Transformers, cascading self-attention modules can capture long-range feature dependencies but tend to overlook the details of local features. … Read more