SOTAVerified

Scene Classification

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

Papers

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Showing 101–125 of 453 papers

TitleStatusHype
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image ScenesCode0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Human-annotated label noise and their impact on ConvNets for remote sensing image scene classification—0
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms—0
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
Vision-Language Models in Remote Sensing: Current Progress and Future TrendsCode1
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth OrbitCode0
Compressing audio CNNs with graph centrality based filter pruning—0
Unsupervised Improvement of Audio-Text Cross-Modal RepresentationsCode0
Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification—0
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster ImagesCode0
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image UnderstandingCode1
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence—0
APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIPCode1
Efficient CNNs via Passive Filter Pruning—0
Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification—0
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning PerspectiveCode2
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification—0
Incremental Learning of Acoustic Scenes and Sound Events—0
Remote Sensing Scene Classification with Masked Image Modeling (MIM)—0
A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification—0
Short-Term Memory Convolutions—0
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification—0
Universal Domain Adaptation for Remote Sensing Image Scene ClassificationCode1
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All datasetsUC Merced Land Use DatasetPlaces365-Standard

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1µ2Net+ (ViT-L/16)Accuracy (%)100—Unverified
2AGOSAccuracy (%)99.88—Unverified
3LSE-NetAccuracy (%)99.78—Unverified
4ResNet50Accuracy (%)99.61—Unverified
5MSMatchAccuracy (%)98.33—Unverified
6MIDC-NetAccuracy (%)97.4—Unverified
#ModelMetricClaimedVerifiedStatus
1iSQRT-COV-Net (ResNet-50)Top 1 Error43.68—Unverified
2WaveMixTop 1 Error43.55—Unverified