SOTAVerified

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 176–200 of 453 papers

TitleStatusHype
CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions—0
Aerial Flood Scene Classification Using Fine-Tuned Attention-based Architecture for Flood-Prone Countries in South Asia—0
Improving Acoustic Scene Classification with City Features—0
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting—0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
Adversarial Training For Sketch Retrieval—0
Change-point Detection Methods for Body-Worn Video—0
CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning—0
Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification—0
An evaluation of data augmentation methods for sound scene geotagging—0
Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices—0
Cascade Learning Localises Discriminant Features in Visual Scene Classification—0
CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence—0
An ensemble learning method for scene classification based on Hidden Markov Model image representation—0
Environmental sound analysis with mixup based multitask learning and cross-task fusion—0
EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition—0
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification—0
Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation—0
Capturing scattered discriminative information using a deep architecture in acoustic scene classification—0
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition—0
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN—0
Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug—0
Exploiting Context for Robustness to Label Noise in Active Learning—0
Acoustic Scene Classification Based on a Large-margin Factorized CNN—0
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification—0
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All datasetsUC Merced Land Use DatasetPlaces365-Standard

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1µ2Net+ (ViT-L/16)Accuracy (%)100—Unverified
2AGOSAccuracy (%)99.88—Unverified
3LSE-NetAccuracy (%)99.78—Unverified
4ResNet50Accuracy (%)99.61—Unverified
5MSMatchAccuracy (%)98.33—Unverified
6MIDC-NetAccuracy (%)97.4—Unverified
#ModelMetricClaimedVerifiedStatus
1iSQRT-COV-Net (ResNet-50)Top 1 Error43.68—Unverified
2WaveMixTop 1 Error43.55—Unverified