Style-Based Neural Architectures for Real-Time Weather Classification

• URL: http://arxiv.org/abs/2604.18251v1
• Date: Mon, 20 Apr 2026 13:28:24 GMT
• Title: Style-Based Neural Architectures for Real-Time Weather Classification
• Abstract summary: We present three neural network architectures designed for real-time classification of weather conditions from images.<n>One model, called "Multi-PatchGAN", is based on PatchGANs used in well-known architectures such as Pix2Pix and CycleGAN.<n>The second model, "Truncated ResNet50", is a simplified version of ResNet50 retaining only its first nine layers.
• Score: 2.7837697086152122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present three neural network architectures designed for real-time classification of weather conditions (sunny, rain, snow, fog) from images. These models, inspired by recent advances in style transfer, aim to capture the stylistic elements present in images. One model, called "Multi-PatchGAN", is based on PatchGANs used in well-known architectures such as Pix2Pix and CycleGAN, but here adapted with multiple patch sizes for detection tasks. The second model, "Truncated ResNet50", is a simplified version of ResNet50 retaining only its first nine layers. This truncation, determined by an evolutionary algorithm, facilitates the extraction of high-frequency features essential for capturing subtle stylistic details. Finally, we propose "Truncated ResNet50 with Gram Matrix and Attention", which computes Gram matrices for each layer during training and automatically weights them via an attention mechanism, thus optimizing the extraction of the most relevant stylistic expressions for classification. These last two models outperform the state of the art and demonstrate remarkable generalization capability on several public databases. Although developed for weather detection, these architectures are also suitable for other appearance-based classification tasks, such as animal species recognition, texture classification, disease detection in medical imaging, or industrial defect identification.

Related papers

• RATLIP: Generative Adversarial CLIP Text-to-Image Synthesis Based on Recurrent Affine Transformations [0.0]
  Conditional affine transformations (CAT) have been applied to different layers of GAN to control content synthesis in images.<n>We first model CAT and a recurrent neural network (RAT) to ensure that different layers can access global information.<n>We then introduce shuffle attention between RAT to mitigate the characteristic of information forgetting in recurrent neural networks.
  arXiv  Detail & Related papers  (2024-05-13T18:49:18Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Dual-Activated Lightweight Attention ResNet50 for Automatic Histopathology Breast Cancer Image Classification [0.0]
  This study introduces a novel method for breast cancer classification, the Dual-Activated Lightweight Attention ResNet50 model. It integrates a pre-trained ResNet50 model with a lightweight attention mechanism, embedding an attention module in the fourth layer of ResNet50. The DALAResNet50 method was tested on breast cancer histopathology images from the BreakHis Database across magnification factors of 40X, 100X, 200X, and 400X, achieving accuracies of 98.5%, 98.7%, 97.9%, and 94.3%, respectively.
  arXiv  Detail & Related papers  (2023-08-25T03:08:41Z)
• Efficient and Explicit Modelling of Image Hierarchies for Image Restoration [120.35246456398738]
  We propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. Inspired by that, we propose the anchored stripe self-attention which achieves a good balance between the space and time complexity of self-attention. Then we propose a new network architecture dubbed GRL to explicitly model image hierarchies in the Global, Regional, and Local range.
  arXiv  Detail & Related papers  (2023-03-01T18:59:29Z)
• DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator [53.57431705309919]
  ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
  arXiv  Detail & Related papers  (2022-11-09T14:57:27Z)
• Multilayer deep feature extraction for visual texture recognition [0.0]
  This paper is focused on improving the accuracy of convolutional neural networks in texture classification. It is done by extracting features from multiple convolutional layers of a pretrained neural network and aggregating such features using Fisher vector. We verify the effectiveness of our method on texture classification of benchmark datasets, as well as on a practical task of Brazilian plant species identification.
  arXiv  Detail & Related papers  (2022-08-22T03:53:43Z)
• Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder [0.0]
  We exploit advances in Variational Autoencoders to investigate the early visual cortex with sparse coding hierarchical VAEs trained on natural images. We show that representations similar to the one found in the primary and secondary visual cortices naturally emerge under mild inductive biases. We show that a neuroscience-inspired choice of the recognition model is critical for two signatures of computations with generative models.
  arXiv  Detail & Related papers  (2022-06-01T12:21:58Z)
• ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization [71.91942002659795]
  We investigate and adapt network quantization techniques to accelerate inference and enable its use on compute limited platforms. ZippyPoint, our efficient quantized network with binary descriptors, improves the network runtime speed, the descriptor matching speed, and the 3D model size. These improvements come at a minor performance degradation as evaluated on the tasks of homography estimation, visual localization, and map-free visual relocalization.
  arXiv  Detail & Related papers  (2022-03-07T18:59:03Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)
• Deep learning for lithological classification of carbonate rock micro-CT images [52.77024349608834]
  This work intends to present an application of deep learning techniques to identify patterns in Brazilian pre-salt carbonate rock microtomographic images. Four convolutional neural network models were proposed. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second.
  arXiv  Detail & Related papers  (2020-07-30T19:14:00Z)
• Identity Enhanced Residual Image Denoising [61.75610647978973]
  We learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising. The proposed network produces remarkably higher numerical accuracy and better visual image quality than the classical state-of-the-art and CNN algorithms.
  arXiv  Detail & Related papers  (2020-04-26T04:52:22Z)
• Large-scale spatiotemporal photonic reservoir computer for image classification [0.8701566919381222]
  We propose a scalable photonic architecture for implementation of feedforward and recurrent neural networks to perform the classification of handwritten digits. Our experiment exploits off-the-shelf optical and electronic components to currently achieve a network size of 16,384 nodes.
  arXiv  Detail & Related papers  (2020-04-06T10:22:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.