Papers Scene Classification
“Scene Classification” 태그가 달린 논문 497편 · 필터 해제
AVSD-Scenes: A Dataset for Audio-Visual Description of Urban Scenes
Natural language descriptions can provide rich semantic representations of audio-visual urban scenes, yet datasets that jointly describe both auditory and visual information remain limited. In this paper, we introduce AV…
Cross-Modal RetrievalScene ClassificationSemi-Supervised Adaptation of Vision-Language Models for Image Classification
Vision-language models like CLIP have shown sig- nificant potential in handling natural images, yet their perfor- mance is often limited by the distinct characteristics of satellite imagery. While parameter-efficient ada…
Scene ClassificationImage ClassificationLabel-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification
Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training. Feature-statistics augmentation such as MixStyle,…
Multi-Label ClassificationDomain GeneralizationScene ClassificationPromptable Concept Segmentation from Above: Evaluating SAM 3's Zero-Shot and One-Shot Capabilities in Remote Sensing
The deployment of large-scale foundation models, such as the Segment Anything Model 3 (SAM 3), promises a transition toward open-vocabulary, training-free computer vision. However, their capacity to generalize out-of-dis…
Instance SegmentationScene ClassificationObject DetectionTerraDiT-$Ω$: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive
Generative models have achieved remarkable progress, yet applying them to satellite imagery remains challenging. Unlike natural imagery, satellite scenes are structured by spatially complex and semantically distinct geom…
Scene ClassificationData AugmentationObject DetectionMSNN-LINet: Cross-Modal Learning via Continuous Linear Integration
We present LINet (Linear Integration Network), a Multi-Stream Neural Network (MSNN) for RGB-D scene classification. Current multi-modal architectures treat feature fusion as a discrete, ad-hoc event: early fusion entangl…
Scene ClassificationWeGenBench: A Multidimensional Diagnostic Benchmark towards Text-to-Image Model Optimization
Recent text-to-image generation models have demonstrated remarkable capabilities in synthesizing highly realistic images from text inputs alone. Although existing benchmarks can evaluate the generation capabilities of va…
Text-to-Image GenerationScene ClassificationFLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales
Foundation models offer a promising route to transferable remote sensing representations, but many current approaches depend on very large pretraining datasets and fixed sensor configurations, limiting their suitability …
Scene ClassificationVision Transformers and Convolutional Neural Networks for Land Use Scene Classification
Land Use Scene Classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable resource management. In recent years, deep learning methods have signifi…
Scene ClassificationUrban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception
We present Urban-ImageNet, a large-scale multi-modal dataset and evaluation benchmark for urban space perception from user-generated social media imagery. The corpus contains over 2 Million public social media images and…
Cross-Modal RetrievalInstance SegmentationScene ClassificationImage ClassificationEdge-Cloud Collaborative Reconstruction via Structure-Aware Latent Diffusion for Downstream Remote Sensing Perception
The exponential surge in high-resolution remote sensing data faces a severe bottleneck in satellite-to-ground transmission. Limited downlink bandwidth forces the use of extreme high-ratio compression, which irreversibly …
Scene ClassificationObject DetectionSceneSelect: Selective Learning for Trajectory Scene Classification and Expert Scheduling
Accurate trajectory prediction is fundamentally challenging due to high scene heterogeneity - the severe variance in motion velocity, spatial density, and interaction patterns across different real-world environments. Ho…
Trajectory PredictionScene ClassificationWhen Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks
The adoption of vision-language models (VLMs) for wireless network management is accelerating, yet no systematic understanding exists of where these large foundation models outperform lightweight convolutional neural net…
Scene ClassificationRGB-Pointmap Pretraining for Unified 3D Scene Understanding
Pretraining 3D encoders through alignment with Contrastive Language-Image Pre-training (CLIP) has emerged as a promising direction for learning generalizable representations for 3D scene understanding. In this paper, we …
Visual Question AnsweringRepresentation LearningScene ClassificationScene UnderstandingMultimodal Analysis of State-Funded News Coverage of the Israel-Hamas War on YouTube Shorts
YouTube Shorts have become central to news consumption on the platform, yet research on how geopolitical events are represented in this format remains limited. To address this gap, we present a multimodal pipeline that c…
Scene ClassificationSentiment AnalysisSVH-BD : Synthetic Vegetation Hyperspectral Benchmark Dataset for Emulation of Remote Sensing Images
This dataset provides a large collection of 10,915 synthetic hyperspectral image cubes paired with pixel-level vegetation trait maps, designed to support research in radiative transfer emulation, vegetation trait retriev…
Scene ClassificationDual Contrastive Network for Few-Shot Remote Sensing Image Scene Classification
Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote sensing images with only a few labeled samples. The main challenges lie in small inter-class variances and large intra-class varian…
Scene ClassificationContrastive LearningSurvey on Remote Sensing Scene Classification: From Traditional Methods to Large Generative AI Models
Remote sensing scene classification has experienced a paradigmatic transformation from traditional handcrafted feature methods to sophisticated artificial intelligence systems that now form the backbone of modern Earth o…
Synthetic Data GenerationRepresentation LearningDomain GeneralizationScene ClassificationSIGMAE: A Spectral-Index-Guided Foundation Model for Multispectral Remote Sensing
Pretraining and fine-tuning have emerged as a new paradigm in remote sensing image interpretation. Among them, Masked Autoencoder (MAE)-based pretraining stands out for its strong capability to learn general feature repr…
Spectral ReconstructionRepresentation LearningSemantic SegmentationScene ClassificationEfficient RGB-D Scene Understanding via Multi-task Adaptive Learning and Cross-dimensional Feature Guidance
Scene understanding plays a critical role in enabling intelligence and autonomy in robotic systems. Traditional approaches often face challenges, including occlusions, ambiguous boundaries, and the inability to adapt att…
Panoptic SegmentationSemantic SegmentationInstance SegmentationScene Classification