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 411–420 of 453 papers

Title	Date	Tasks	Status
Label Tree Embeddings for Acoustic Scene Classification	Jun 25, 2016	Acoustic Scene ClassificationClassification	—Unverified
Modality and Component Aware Feature Fusion For RGB-D Scene Classification	Jun 1, 2016	General ClassificationObject Recognition	—Unverified
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification	Feb 4, 2016	General ClassificationScene Classification	CodeCode Available
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference	Jan 27, 2016	Activity RecognitionGesture Recognition	—Unverified
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks	Dec 18, 2015	General ClassificationLong-tail Learning	CodeCode Available
Per-Sample Kernel Adaptation for Visual Recognition and Grouping	Dec 1, 2015	Action RecognitionAction Recognition In Videos	—Unverified
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification	Oct 6, 2015	ClusteringGeneral Classification	—Unverified
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks	Sep 22, 2015	ClassificationDiversity	—Unverified
Histogram of gradients of Time-Frequency Representations for Audio scene detection	Aug 20, 2015	General ClassificationScene Classification	—Unverified
A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification	Jun 18, 2015	General 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