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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 141–150 of 453 papers

Title	Date	Tasks	Status
A Deep Incremental Boltzmann Machine for Modeling Context in Robots	Oct 13, 2017	General ClassificationScene Classification	—Unverified
Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition	May 28, 2014	Event DetectionObject	—Unverified
A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene Classification	Jan 19, 2024	Saliency DetectionScene Classification	—Unverified
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification	Jul 3, 2021	Acoustic Scene ClassificationData Augmentation	—Unverified
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features	Jan 17, 2025	Language ModelingLanguage Modelling	—Unverified
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification	Nov 11, 2016	ClassificationGeneral Classification	—Unverified
Acoustic Scene Classification	Nov 13, 2014	Acoustic Scene ClassificationClassification	—Unverified
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems	Jan 23, 2024	Scene ClassificationScene Recognition	—Unverified
Automatic Monitoring of Activities of Daily Living based on Real-life Acoustic Sensor Data: a preliminary study	Aug 1, 2013	Acoustic Scene ClassificationScene Classification	—Unverified
Automated Vision-based Bridge Component Extraction Using Multiscale Convolutional Neural Networks	May 15, 2018	ClassificationGeneral 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