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 51–75 of 453 papers

TitleStatusHype
Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene ClassificationCode1
APLA: A Simple Adaptation Method for Vision TransformersCode1
BYOL-S: Learning Self-supervised Speech Representations by BootstrappingCode1
ApproxDet: Content and Contention-Aware Approximate Object Detection for MobilesCode1
Spectrum Correction: Acoustic Scene Classification with Mismatched Recording DevicesCode1
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image UnderstandingCode1
Do we still need ImageNet pre-training in remote sensing scene classification?Code1
Understanding the Role of Individual Units in a Deep Neural NetworkCode1
Universal Domain Adaptation for Remote Sensing Image Scene ClassificationCode1
Vision-based Fight Detection from Surveillance CamerasCode1
WaveMix: A Resource-efficient Neural Network for Image AnalysisCode1
WaveMix-Lite: A Resource-efficient Neural Network for Image AnalysisCode1
Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial VehiclesCode1
Receptive-field-regularized CNN variants for acoustic scene classificationCode1
A system of vision sensor based deep neural networks for complex driving scene analysis in support of crash risk assessment and preventionCode1
CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer VisionCode1
Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing DomainCode1
Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy EnvironmentsCode1
Debiased Self-Training for Semi-Supervised LearningCode1
A Two-Stage Approach to Device-Robust Acoustic Scene ClassificationCode1
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition—0
A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification—0
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification—0
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition—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