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

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Showing 421–430 of 453 papers

Title	Date	Tasks	Status
Towards automatic initialization of registration algorithms using simulated endoscopy images	Jun 28, 2018	AnatomyScene Classification	CodeCode Available
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification	Feb 4, 2016	General ClassificationScene Classification	CodeCode Available
Pairwise Comparison Network for Remote Sensing Scene Classification	May 17, 2022	ClassificationScene Classification	CodeCode Available
Parsing Natural Scenes and Natural Language with Recursive Neural Networks	Jun 1, 2011	General ClassificationScene Classification	CodeCode Available
Vanishing Depth: A Depth Adapter with Positional Depth Encoding for Generalized Image Encoders	Mar 25, 2025	6D Pose Estimation using RGBDepth Completion	CodeCode Available
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer	Oct 16, 2021	Acoustic Scene ClassificationScene Classification	CodeCode Available
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
A Challenge to Build Neuro-Symbolic Video Agents	May 20, 2025	Scene ClassificationVideo Retrieval	CodeCode Available
Efficient Multi-Resolution Fusion for Remote Sensing Data with Label Uncertainty	Feb 7, 2024	Scene Classification	CodeCode Available
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images	Apr 21, 2023	ClassificationComputational Efficiency	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