Scene Classification
121 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Datasets
RESISC45
BigEarthNet
Places365
RSICD
DCASE 2016
AID
Million-AID
Latest papers with no code
Pretraining Billion-scale Geospatial Foundational Models on Frontier
Although large FMs have demonstrated significant impact in natural language processing and computer vision, efforts toward FMs for geospatial applications have been restricted to smaller size models, as pretraining larger models requires very large computing resources equipped with state-of-the-art hardware accelerators.
Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification
Hence, a novel approach that uses a semantic segmentation mask to provide Hu-moments-based segmentation categories' shape characterization, designated by Segmentation-based Hu-Moments Features (SHMFs), is proposed.
Neural Embedding Compression For Efficient Multi-Task Earth Observation Modelling
We introduce Neural Embedding Compression (NEC), based on the transfer of compressed embeddings to data consumers instead of raw data.
Leveraging feature communication in federated learning for remote sensing image classification
In the realm of Federated Learning (FL) applied to remote sensing image classification, this study introduces and assesses several innovative communication strategies.
Leveraging Self-Supervised Learning for Scene Recognition in Child Sexual Abuse Imagery
In light of that, reliable automated tools that can securely and efficiently deal with this data are paramount.
Knowledge-Aware Neuron Interpretation for Scene Classification
Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts.
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori
In this work, we aim to establish a Bayesian adaptive learning framework by focusing on estimating latent variables in deep neural network (DNN) models.
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems
By mitigating the bias in the computer vision pipelines, we can ensure fairer and more equitable outcomes for applied computer vision, including home valuation and smart home security systems.
A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene Classification
Thus, tasks such as scene classification, identification, indexing, etc., can be performed efficiently using the unique summary.
Kronecker Product Feature Fusion for Convolutional Neural Network in Remote Sensing Scene Classification
Remote Sensing Scene Classification is a challenging and valuable research topic, in which Convolutional Neural Network (CNN) has played a crucial role.
