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
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene ClassificationCode1
APLA: A Simple Adaptation Method for Vision TransformersCode1
APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIPCode1
ApproxDet: Content and Contention-Aware Approximate Object Detection for MobilesCode1
Efficient Multi-Task Scene Analysis with RGB-D TransformersCode1
Efficient Multi-Task RGB-D Scene Analysis for Indoor EnvironmentsCode1
Emergent Properties of Foveated Perceptual SystemsCode1
Universal Adversarial Examples in Remote Sensing: Methodology and BenchmarkCode1
A system of vision sensor based deep neural networks for complex driving scene analysis in support of crash risk assessment and preventionCode1
Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy EnvironmentsCode1
WaveMix: A Resource-efficient Neural Network for Image AnalysisCode1
WaveMix-Lite: A Resource-efficient Neural Network for Image AnalysisCode1
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene ClassificationCode1
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile ApplicationCode1
Generic Knowledge Boosted Pre-training For Remote Sensing ImagesCode1
Backdoor Attacks for Remote Sensing Data with Wavelet TransformCode1
Leveraging Large (Visual) Language Models for Robot 3D Scene UnderstandingCode1
Receptive-field-regularized CNN variants for acoustic scene classificationCode1
BYOL-S: Learning Self-supervised Speech Representations by BootstrappingCode1
A Two-Stage Approach to Device-Robust Acoustic Scene ClassificationCode1
Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing DomainCode1
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene ClassificationCode0
Equivariant Multi-View NetworksCode0
Exploring Models and Data for Remote Sensing Image Caption GenerationCode0
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
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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