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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–325 of 453 papers

TitleStatusHype
Environmental Sound Classification with Parallel Temporal-spectral Attention—0
Deliberative Explanations: visualizing network insecuritiesCode0
A Discriminative Learned CNN Embedding for Remote Sensing Image Scene Classification—0
In-domain representation learning for remote sensingCode0
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting—0
Centroid Based Concept Learning for RGB-D Indoor Scene ClassificationCode0
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image ClassificationCode0
Cross-task pre-training for on-device acoustic scene classification—0
Acoustic Scene Classification Based on a Large-margin Factorized CNN—0
Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification—0
Acoustic scene analysis with multi-head attention networksCode0
Semantic-Aware Scene RecognitionCode0
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene ClassificationCode0
Online Sensor Hallucination via Knowledge Distillation for Multimodal Image Classification—0
Multiple instance dense connected convolution neural network for aerial image scene classification—0
Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings—0
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning—0
City classification from multiple real-world sound scenesCode0
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling—0
Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge—0
CNN depth analysis with different channel inputs for Acoustic Scene Classification—0
Few-Shot Learning with Per-Sample Rich Supervision—0
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding—0
Dynamic Traffic Scene Classification with Space-Time Coherence—0
Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes—0
Show:102550
← PrevPage 13 of 19Next →
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