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Scene Classification
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Challenge to Build Neuro-Symbolic Video Agents
EarthSynth: Generating Informative Earth Observation with Diffusion Models
Energy efficiency analysis of Spiking Neural Networks for space applications
Minimizing Risk Through Minimizing Model-Data Interaction: A Protocol For Relying on Proxy Tasks When Designing Child Sexual Abuse Imagery Detection Models
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge
FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote Sensing
FlexiMo: A Flexible Remote Sensing Foundation Model
Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding in Indoor Building Environments
Vanishing Depth: A Depth Adapter with Positional Depth Encoding for Generalized Image Encoders
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images
Improving Acoustic Scene Classification with City Features
Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing
MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free Remote Sensing Imagery
APLA: A Simple Adaptation Method for Vision Transformers
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification
RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer
LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing Images
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models
Improving Acoustic Scene Classification in Low-Resource Conditions
UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning
Multimodal Remote Sensing Scene Classification Using VLMs and Dual-Cross Attention Networks
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2
Aerial Flood Scene Classification Using Fine-Tuned Attention-based Architecture for Flood-Prone Countries in South Asia
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
Less yet robust: crucial region selection for scene recognition
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction
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