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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 453 papers

TitleStatusHype
Deep Discriminative Model for Video Classification—0
In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization—0
Satellite Image Scene Classification via ConvNet with Context Aggregation—0
A punishment voting algorithm based on super categories construction for acoustic scene classification—0
Towards automatic initialization of registration algorithms using simulated endoscopy imagesCode0
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
Convex Class Model on Symmetric Positive Definite Manifolds—0
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network—0
Recent advances and opportunities in scene classification of aerial images with deep models—0
AID++: An Updated Version of AID on Scene Classification—0
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network—0
Automated Vision-based Bridge Component Extraction Using Multiscale Convolutional Neural Networks—0
Vision-based Automated Bridge Component Recognition Integrated With High-level Scene Understanding—0
Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey—0
HexaConvCode0
Scenarios: A New Representation for Complex Scene Understanding—0
Exploring the significance of using perceptually relevant image decolorization method for scene classification—0
Large-Scale 3D Scene Classification With Multi-View Volumetric CNN—0
Exploring Models and Data for Remote Sensing Image Caption GenerationCode0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
What is (missing or wrong) in the scene? A Hybrid Deep Boltzmann Machine For Contextualized Scene Modeling—0
A Deep Incremental Boltzmann Machine for Modeling Context in Robots—0
Deep Scene Image Classification With the MFAFVNet—0
Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models—0
Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization—0
Traffic scene recognition based on deep cnn and vlad spatial pyramids—0
Zero-Shot Deep Domain Adaptation—0
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification—0
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification—0
Object-Level Context Modeling For Scene Classification with Context-CNN—0
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization—0
DeepCorrect: Correcting DNN models against Image DistortionsCode0
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks—0
The 2017 DAVIS Challenge on Video Object Segmentation—0
Audio Scene Classification with Deep Recurrent Neural Networks—0
Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embeddingCode0
Remote Sensing Image Scene Classification: Benchmark and State of the ArtCode1
Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition—0
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory—0
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 2 Validation—0
Large Margin Discriminant Dimensionality Reduction in Prediction Space—0
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs—0
Object based Scene Representations using Fisher Scores of Local Subspace Projections—0
ResFeats: Residual Network Based Features for Image Classification—0
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification—0
Change-point Detection Methods for Body-Worn Video—0
Regularized Optimal Transport and the Rot Mover's Distance—0
Mapping Between fMRI Responses to Movies and their Natural Language AnnotationsCode0
Places: An Image Database for Deep Scene Understanding—0
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNsCode0
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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