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 241–250 of 453 papers

Title	Date	Tasks	Status
Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation	Jul 30, 2021	Contrastive Learningdomain classification	—Unverified
Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint	Jul 30, 2021	Acoustic Scene ClassificationScene Classification	—Unverified
Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene Classification	Jul 28, 2021	Scene Classification	—Unverified
Over-Parameterization and Generalization in Audio Classification	Jul 19, 2021	Acoustic Scene ClassificationAudio Classification	—Unverified
Local semantic enhanced convnet for aerial scene recognition	Jul 8, 2021	Aerial Scene ClassificationImage Classification	CodeCode Available
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification	Jul 3, 2021	Acoustic Scene ClassificationData Augmentation	—Unverified
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification	Jun 15, 2021	Aerial Scene ClassificationClassification	—Unverified
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems	May 28, 2021	Acoustic Scene ClassificationQuantization	CodeCode Available
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions	May 28, 2021	ClassificationData Augmentation	—Unverified
Multi-Feature Fusion-based Scene Classification Framework for HSR Images	May 22, 2021	ClassificationScene Classification	—Unverified

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All datasets UC Merced Land Use Dataset Places365-Standard

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	µ2Net+ (ViT-L/16)	Accuracy (%)	100	—	Unverified
2	AGOS	Accuracy (%)	99.88	—	Unverified
3	LSE-Net	Accuracy (%)	99.78	—	Unverified
4	ResNet50	Accuracy (%)	99.61	—	Unverified
5	MSMatch	Accuracy (%)	98.33	—	Unverified
6	MIDC-Net	Accuracy (%)	97.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	iSQRT-COV-Net (ResNet-50)	Top 1 Error	43.68	—	Unverified
2	WaveMix	Top 1 Error	43.55	—	Unverified