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 301–325 of 453 papers

TitleStatusHype
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning—0
Human-annotated label noise and their impact on ConvNets for remote sensing image scene classification—0
Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Incremental Learning of Acoustic Scenes and Sound Events—0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey—0
Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech—0
In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization—0
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling—0
Knowledge-Aware Neuron Interpretation for Scene Classification—0
Kronecker Product Feature Fusion for Convolutional Neural Network in Remote Sensing Scene Classification—0
Label Tree Embeddings for Acoustic Scene Classification—0
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks—0
Language-Assisted 3D Scene Understanding—0
Large Margin Discriminant Dimensionality Reduction in Prediction Space—0
Large Margin Learning of Upstream Scene Understanding Models—0
Large-Scale 3D Scene Classification With Multi-View Volumetric CNN—0
Learning Class-to-Image Distance with Object Matchings—0
Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification—0
Environmental Sound Classification with Parallel Temporal-spectral Attention—0
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs—0
Learning Important Spatial Pooling Regions for Scene Classification—0
Learning Instance Representation Banks for Aerial Scene Classification—0
Learning Receptive Fields for Pooling from Tensors of Feature Response—0
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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