Papers Scene Classification
“Scene Classification” 태그가 달린 논문 497편 · 필터 해제
VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing
Recent advancements in Multimodal Large Language Models (MLLMs) have enabled complex reasoning. However, existing remote sensing (RS) benchmarks remain heavily biased toward perception tasks, such as object recognition a…
Scene ClassificationMultimodal ReasoningObject RecognitionRevisiting Aerial Scene Classification on the AID Benchmark
Aerial images play a vital role in urban planning and environmental preservation, as they consist of various structures, representing different types of buildings, forests, mountains, and unoccupied lands. Due to its het…
Scene ClassificationImage ClassificationTowards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion
Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-scale benchmark datasets. However, this reliance on massive training data al…
Scene ClassificationCommunication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning
Semantic communication is emerging as a key enabler for distributed edge intelligence due to its capability to convey task-relevant meaning. However, achieving communication-efficient training and robust inference over w…
Self-Supervised LearningSemantic CommunicationScene ClassificationLeveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Images
Federated Learning (FL) enables collaborative model training while keeping training data localized, allowing us to preserve privacy in various domains including remote sensing. However, recent studies show that FL models…
Remote Sensing Image ClassificationScene ClassificationFederated LearningRS-Prune: Training-Free Data Pruning at High Ratios for Efficient Remote Sensing Diffusion Foundation Models
Diffusion-based remote sensing (RS) generative foundation models are cruial for downstream tasks. However, these models rely on large amounts of globally representative data, which often contain redundancy, noise, and cl…
Scene ClassificationMulti-task Learning with Extended Temporal Shift Module for Temporal Action Localization
We present our solution to the BinEgo-360 Challenge at ICCV 2025, which focuses on temporal action localization (TAL) in multi-perspective and multi-modal video settings. The challenge provides a dataset containing panor…
Temporal Action LocalizationScene ClassificationMulti-Task LearningEnsemble LearningVLM2GeoVec: Toward Universal Multimodal Embeddings for Remote Sensing
Satellite imagery differs fundamentally from natural images: its aerial viewpoint, very high resolution, diverse scale variations, and abundance of small objects demand both region-level spatial reasoning and holistic sc…
Cross-Modal RetrievalScene ClassificationScene UnderstandingQuestion AnsweringSceneMixer: Exploring Convolutional Mixing Networks for Remote Sensing Scene Classification
Remote sensing scene classification plays a key role in Earth observation by enabling the automatic identification of land use and land cover (LULC) patterns from aerial and satellite imagery. Despite recent progress wit…
Scene ClassificationHierarchical Semi-Supervised Active Learning for Remote Sensing
The performance of deep learning models in remote sensing (RS) strongly depends on the availability of high-quality labeled data. However, collecting large-scale annotations is costly and time-consuming, while vast amoun…
Scene ClassificationActive LearningAsymmetric Cross-Modal Knowledge Distillation: Bridging Modalities with Weak Semantic Consistency
Cross-modal Knowledge Distillation has demonstrated promising performance on paired modalities with strong semantic connections, referred to as Symmetric Cross-modal Knowledge Distillation (SCKD). However, implementing S…
Self-Supervised LearningKnowledge DistillationScene ClassificationHierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classification
Remote sensing image scene classification remains a challenging task, primarily due to the complex spatial structures and multi-scale characteristics of ground objects. Although CNN-based methods excel at extracting loca…
Scene ClassificationScene RecognitionFew-Shot Remote Sensing Image Scene Classification with CLIP and Prompt Learning
Remote sensing applications increasingly rely on deep learning for scene classification. However, their performance is often constrained by the scarcity of labeled data and the high cost of annotation across diverse geog…
Scene ClassificationAdaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification
Multi-label classification (MLC) offers a more comprehensive semantic understanding of Remote Sensing (RS) imagery compared to traditional single-label classification (SLC). However, obtaining complete annotations for ML…
Multi-Label ClassificationScene ClassificationMulti-Label LearningFew-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models
Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However, their ability to generalize in low-data …
Scene ClassificationFew-Shot LearningA Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data
Deep learning has gained broad interest in remote sensing image scene classification thanks to the effectiveness of deep neural networks in extracting the semantics from complex data. However, deep networks require large…
Scene ClassificationAttention over Scene Graphs: Indoor Scene Representations Toward CSAI Classification
Indoor scene classification is a critical task in computer vision, with wide-ranging applications that go from robotics to sensitive content analysis, such as child sexual abuse imagery (CSAI) classification. The problem…
Scene ClassificationHydroVision: Predicting Optically Active Parameters in Surface Water Using Computer Vision
Ongoing advancements in computer vision, particularly in pattern recognition and scene classification, have enabled new applications in environmental monitoring. Deep learning now offers non-contact methods for assessing…
Scene ClassificationTransfer LearningContributions to Label-Efficient Learning in Computer Vision and Remote Sensing
This manuscript presents a series of my selected contributions to the topic of label-efficient learning in computer vision and remote sensing. The central focus of this research is to develop and adapt methods that can l…
Semantic SegmentationScene ClassificationContrastive LearningMulti-Task LearningFedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing
Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients), where each client stores data locally and only shares model updates with a central se…
Scene ClassificationImage ClassificationFederated Learning