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 401–410 of 453 papers

Title	Date	Tasks	Status
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs	Oct 4, 2016	General ClassificationScene Classification	CodeCode Available
What makes ImageNet good for transfer learning?	Aug 30, 2016	Action ClassificationGeneral Classification	CodeCode Available
Solving Visual Madlibs with Multiple Cues	Aug 11, 2016	Activity PredictionAttribute	—Unverified
An ensemble learning method for scene classification based on Hidden Markov Model image representation	Jul 22, 2016	Ensemble LearningGeneral Classification	—Unverified
A probabilistic patch based image representation using Conditional Random Field model for image classification	Jul 22, 2016	ClassificationFace Recognition	—Unverified
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks	Jul 19, 2016	ClassificationGeneral Classification	—Unverified
Hierarchical learning for DNN-based acoustic scene classification	Jul 13, 2016	Acoustic Scene ClassificationClassification	—Unverified
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling	Jul 11, 2016	Acoustic Scene ClassificationAll	CodeCode Available
Adversarial Training For Sketch Retrieval	Jul 10, 2016	Image GenerationRetrieval	—Unverified
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification	Jul 1, 2016	ColorizationImage Colorization	CodeCode Available

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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