Scene Classification
122 papers with code • 2 benchmarks • 21 datasets
Scene Classification is a task in which scenes from photographs are categorically classified. Unlike object classification, which focuses on classifying prominent objects in the foreground, Scene Classification uses the layout of objects within the scene, in addition to the ambient context, for classification.
Source: Scene classification with Convolutional Neural Networks
Benchmarks
These leaderboards are used to track progress in Scene Classification
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Datasets
RESISC45
BigEarthNet
Places365
RSICD
DCASE 2016
AID
Million-AID
Latest papers with no code
Language-Assisted 3D Scene Understanding
The scale and quality of point cloud datasets constrain the advancement of point cloud learning.
CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence
Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way.
Cascade Learning Localises Discriminant Features in Visual Scene Classification
In this work, we investigate the localisation of features learned via two varied learning paradigms and demonstrate the superiority of one learning approach with respect to localisation.
SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
Unlocking the capabilities of explainable fewshot learning in remote sensing
While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies.
Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs
We present a locality-aware method for interpreting the latent space of wavelet-based Generative Adversarial Networks (GANs), that can well capture the large spatial and spectral variability that is characteristic to satellite imagery.
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification
In order to tackle these issues, we turn to the recently proposed parameter-efficient tuning methods, such as VPT, which updates only the newly added prompt parameters while keeping the pre-trained backbone frozen.
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
After that, a universal adversarial purification framework is developed using the forward and reverse process of the pre-trained diffusion models to purify the perturbations from adversarial samples.
Visual Saliency Detection in Advanced Driver Assistance Systems
A dedicated 1D temporal deep convolutional network has been devised to classify the collected PPG time-series, enabling us to assess the driver level of attentiveness.
Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type
The influence of human tasks strongly depended on image type: For objects, human manual selection produced maps that were most similar to CNN, while the specific eye movement task has little impact.
