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 401–425 of 453 papers

TitleStatusHype
Deep Learning for Scene Classification: A Survey—0
Deep Neural Decision Forest for Acoustic Scene Classification—0
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding—0
Deep Scene Image Classification With the MFAFVNet—0
Deep Space Separable Distillation for Lightweight Acoustic Scene Classification—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning—0
DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry—0
Dense v.s. Sparse: A Comparative Study of Sampling Analysis in Scene Classification of High-Resolution Remote Sensing Imagery—0
Deriving Visual Semantics from Spatial Context: An Adaptation of LSA and Word2Vec to generate Object and Scene Embeddings from Images—0
Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition—0
L_2BN: Enhancing Batch Normalization by Equalizing the L_2 Norms of Features—0
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems—0
Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification—0
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
Dynamic loss balancing and sequential enhancement for road-safety assessment and traffic scene classification—0
Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis—0
Dynamic texture and scene classification by transferring deep image features—0
Dynamic Traffic Scene Classification with Space-Time Coherence—0
EarthSynth: Generating Informative Earth Observation with Diffusion Models—0
Efficient CNNs via Passive Filter Pruning—0
Embedded Self-Distillation in Compact Multi-Branch Ensemble Network for Remote Sensing Scene Classification—0
Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification—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