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 421–430 of 453 papers

Title	Date	Tasks	Status
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition	Jun 17, 2015	ObjectObject Recognition	—Unverified
Reflection Invariance: an important consideration of image orientation	Jun 8, 2015	General Classificationobject-detection	—Unverified
Second-Order Constrained Parametric Proposals and Sequential Search-Based Structured Prediction for Semantic Segmentation in RGB-D Images	Jun 1, 2015	Scene ClassificationSegmentation	—Unverified
Scene Classification With Semantic Fisher Vectors	Jun 1, 2015	ClassificationGeneral Classification	—Unverified
Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification	Jun 1, 2015	General Classificationobject-detection	—Unverified
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models	May 2, 2015	ClassificationClustering	—Unverified
SA-CNN: Dynamic Scene Classification using Convolutional Neural Networks	Feb 17, 2015	ClassificationGeneral Classification	—Unverified
Dense v.s. Sparse: A Comparative Study of Sampling Analysis in Scene Classification of High-Resolution Remote Sensing Imagery	Feb 4, 2015	ClassificationGeneral Classification	—Unverified
Dynamic texture and scene classification by transferring deep image features	Feb 1, 2015	ClassificationGeneral Classification	—Unverified
Object Detectors Emerge in Deep Scene CNNs	Dec 22, 2014	General ClassificationObject	CodeCode Available

Show:10 25 50

← PrevPage 43 of 46Next →

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