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 371–380 of 453 papers

Title	Date	Tasks	Status
What is (missing or wrong) in the scene? A Hybrid Deep Boltzmann Machine For Contextualized Scene Modeling	Oct 16, 2017	General ClassificationScene Classification	—Unverified
A Deep Incremental Boltzmann Machine for Modeling Context in Robots	Oct 13, 2017	General ClassificationScene Classification	—Unverified
Deep Scene Image Classification With the MFAFVNet	Oct 1, 2017	ClassificationGeneral Classification	—Unverified
Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models	Aug 4, 2017	General ClassificationScene Classification	—Unverified
Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization	Aug 4, 2017	ClusteringMetric Learning	—Unverified
Traffic scene recognition based on deep cnn and vlad spatial pyramids	Jul 24, 2017	Region ProposalScene Classification	—Unverified
Zero-Shot Deep Domain Adaptation	Jul 6, 2017	ClassificationDomain Adaptation	—Unverified
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification	Jun 20, 2017	Acoustic Scene ClassificationGeneral Classification	—Unverified
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification	Jun 5, 2017	Aerial Scene ClassificationGeneral Classification	—Unverified
Object-Level Context Modeling For Scene Classification with Context-CNN	May 11, 2017	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