Scene Classification
123 papers with code • 2 benchmarks • 22 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
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
lzw-lzw/awesome-remote-sensing-vision-language-models
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Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships.
Unsupervised Improvement of Audio-Text Cross-Modal Representations
In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio.
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts.
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding
We argue that this learning strategy is suboptimal in the realm of RS, since the required representations for different RS downstream tasks are often varied and complex.
APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP
APPLeNet emphasizes the importance of multi-scale feature learning in RS scene classification and disentangles visual style and content primitives for domain generalization tasks.
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information.
Universal Domain Adaptation for Remote Sensing Image Scene Classification
zhu-xlab/unida
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Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data is available or not.
Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images
Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible.
Backdoor Attacks for Remote Sensing Data with Wavelet Transform
ndraeger/waba
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Despite its simplicity, the proposed method can significantly cheat the current state-of-the-art deep learning models with a high attack success rate.
CochlScene: Acquisition of acoustic scene data using crowdsourcing
This paper describes a pipeline for collecting acoustic scene data by using crowdsourcing.
