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

Showing 1–25 of 453 papers

TitleStatusHype
Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition—0
A Challenge to Build Neuro-Symbolic Video AgentsCode0
EarthSynth: Generating Informative Earth Observation with Diffusion Models—0
Energy efficiency analysis of Spiking Neural Networks for space applications—0
Minimizing Risk Through Minimizing Model-Data Interaction: A Protocol For Relying on Proxy Tasks When Designing Child Sexual Abuse Imagery Detection Models—0
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 ChallengeCode0
FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote SensingCode0
FlexiMo: A Flexible Remote Sensing Foundation Model—0
Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding in Indoor Building Environments—0
Vanishing Depth: A Depth Adapter with Positional Depth Encoding for Generalized Image EncodersCode0
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images—0
Improving Acoustic Scene Classification with City Features—0
Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification—0
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing—0
MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free Remote Sensing Imagery—0
APLA: A Simple Adaptation Method for Vision TransformersCode1
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification—0
RoMA: Scaling up Mamba-based Foundation Models for Remote SensingCode2
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer—0
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features—0
LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual TasksCode2
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments—0
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution—0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
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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