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 171–180 of 453 papers

TitleStatusHype
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification—0
BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification—0
Audio Scene Classification with Deep Recurrent Neural Networks—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
Deep Learning for Scene Classification: A Survey—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