Click the above “Beginner’s Guide to Vision” to select “Star” or “Top“. Important content delivered at the first time. In the next decade, computer vision will make significant progress. In this article, we will explore the development trends and breakthroughs in computer vision from 2010 to 2020, as well as future goals for computer vision. … Read more
Overview Understand the state-of-the-art transformer models. Learn how we implement transformers for the image captioning problem we have seen using TensorFlow. Compare the results of transformers with attention models. Introduction We have seen that the attention mechanism has become a compelling component of various tasks (such as image captioning) in sequence modeling and transduction models, … Read more
Click on "Xiaobai Learns Vision" above, select to add "Star" or "Top" Heavyweight content delivered first License Plate Recognition Overview License plate recognition based on deep learning, where the vehicle detection network directly uses YOLO for detection. Then, a network is used to detect and recognize the license plate number. The license plate detection network … Read more
Research Progress on Multimodal Named Entity Recognition Methods Wang Hairong1,2, Xu Xi1, Wang Tong1, Jing Boxiang1 1. School of Computer Science and Engineering, Northern Minzu University; 2. Key Laboratory of Intelligent Processing of Image and Graphics, Northern Minzu University Click “Read the Original” at the end of the article to view the literature! Table of … Read more
Source: Algorithm Advancement This article is approximately 4100 words and is recommended to be read in 8 minutes. LongLLaVA performs excellently in long-context multimodal understanding. The authors of this article come from The Chinese University of Hong Kong, Shenzhen, and the Shenzhen Big Data Research Institute. The first authors are PhD student Wang Xidong and … Read more
We have discussed computer vision (or more narrowly, machine vision) before. Last year, the cover story of Electronic Engineering Magazine also talked about computer vision, but the content at that time was more focused on how computers acquire and understand image information from the outside world, leaning towards the perception aspect. According to the definition … Read more
Click the “ReadingPapers” card below to get daily interpretations of top journal papers. Paper Information Abstract Unsupervised learning, which learns data distribution without labeled samples, is a very promising method to solve the challenging task of hyperspectral pan-sharpening. Inspired by this, we introduce an innovative Generative Adversarial Network framework (named MFT-GAN), which integrates transformer networks … Read more
Approximately 10,000 words, recommended reading time: 15 minutes. This article shares the top 10 models in deep learning, which hold significant positions in terms of innovation, application value, and impact. Since the concept of deep learning was proposed in 2006, nearly 20 years have passed. Deep learning, as a revolution in the field of artificial … Read more
Reprinted from Algorithm Advancement With the popularity of models like Sora, diffusion, and GPT, deep generative models have once again become the focus of attention. Deep generative models are a class of powerful machine learning tools that can learn the underlying distribution of input data and generate new sample data similar to the training data. … Read more
Click the blue text Follow us Cite this article: Lyu, P. M., T. Tang, F. H. Ling, J.-J. Luo, N. Boers, W. L. Ouyang, and L. Bai, 2024: ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-024-3316-6 Download: http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-024-3316-6 AI Special Issue | Pre-Publication Long-Term ENSO Forecasting Using … Read more