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
“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
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
Author丨New Intelligence Source丨New Intelligence Editor丨Extreme City Platform Extreme City Guide What factors affect the ability of neural networks to fit data?Are CNNs always worse than Transformers?What magical effects do ReLU and SGD have?Recently, a work involving LeCun has shown us how flexible neural networks are in practice. Artificial intelligence is flourishing today; large models dominate … Read more
Follow the official account “ML_NLP“ Set as “Starred“, heavy content delivered to you first! Reprinted from | Smarter From DETR to ViT, various works have validated the potential of Transformers in the field of computer vision. Naturally, this raises a new question: which is better for image feature extraction, CNN or Transformer? The advantage of … Read more
In the field of maritime transportation, ensuring the safe navigation of vessels is crucial. To prevent maritime accidents, Professor Guo Jian’s team from the Information Engineering University of the People’s Liberation Army of China has developed a novel anomaly detection technology for ship trajectories based on artificial intelligence. This technology utilizes a model known as … Read more