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 401–450 of 453 papers

TitleStatusHype
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNsCode0
What makes ImageNet good for transfer learning?Code0
Solving Visual Madlibs with Multiple Cues—0
An ensemble learning method for scene classification based on Hidden Markov Model image representation—0
A probabilistic patch based image representation using Conditional Random Field model for image classification—0
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks—0
Hierarchical learning for DNN-based acoustic scene classification—0
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global PoolingCode0
Adversarial Training For Sketch Retrieval—0
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classificationCode0
Label Tree Embeddings for Acoustic Scene Classification—0
Modality and Component Aware Feature Fusion For RGB-D Scene Classification—0
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene ClassificationCode0
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference—0
Relay Backpropagation for Effective Learning of Deep Convolutional Neural NetworksCode0
Per-Sample Kernel Adaptation for Visual Recognition and Grouping—0
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification—0
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks—0
Histogram of gradients of Time-Frequency Representations for Audio scene detection—0
A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification—0
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition—0
Reflection Invariance: an important consideration of image orientation—0
Second-Order Constrained Parametric Proposals and Sequential Search-Based Structured Prediction for Semantic Segmentation in RGB-D Images—0
Scene Classification With Semantic Fisher Vectors—0
Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification—0
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models—0
SA-CNN: Dynamic Scene Classification using Convolutional Neural Networks—0
Dense v.s. Sparse: A Comparative Study of Sampling Analysis in Scene Classification of High-Resolution Remote Sensing Imagery—0
Dynamic texture and scene classification by transferring deep image features—0
Object Detectors Emerge in Deep Scene CNNsCode0
Acoustic Scene Classification—0
High Order Structure Descriptors for Scene Images—0
Scalable Multitask Representation Learning for Scene Classification—0
Orientational Pyramid Matching for Recognizing Indoor Scenes—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Learning Important Spatial Pooling Regions for Scene Classification—0
What are You Talking About? Text-to-Image Coreference—0
Learning Receptive Fields for Pooling from Tensors of Feature Response—0
Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition—0
ConceptVision: A Flexible Scene Classification Framework—0
Constrained Parametric Proposals and Pooling Methods for Semantic Segmentation in RGB-D Images—0
Mid-level Visual Element Discovery as Discriminative Mode Seeking—0
Automatic Monitoring of Activities of Daily Living based on Real-life Acoustic Sensor Data: a preliminary study—0
Understanding Indoor Scenes Using 3D Geometric Phrases—0
BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification—0
Blocks That Shout: Distinctive Parts for Scene Classification—0
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images—0
Learning Class-to-Image Distance with Object Matchings—0
Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis—0
Sparse Output Coding for Large-Scale Visual Recognition—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