Papers Scene Classification
“Scene Classification” 태그가 달린 논문 497편 · 필터 해제
Language-Assisted 3D Scene Understanding
The scale and quality of point cloud datasets constrain the advancement of point cloud learning. Recently, with the development of multi-modal learning, the incorporation of domain-agnostic prior knowledge from other mod…
3D Object Detection3D Semantic SegmentationDimensionality Reductionfeature selection+6CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence
Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way. The emergence of the 3rd wave information has made a proportion of maps are available to be generated ubiquitousl…
RetrievalScene ClassificationSuper-Resolutiontext annotationGeoChat: Grounded Large Vision-Language Model for Remote Sensing
Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However, such general-domain VLMs perform poorly…
Instruction FollowingLanguage ModelingLanguage ModellingQuestion Answering+2Cascade Learning Localises Discriminant Features in Visual Scene Classification
Lack of interpretability of deep convolutional neural networks (DCNN) is a well-known problem particularly in the medical domain as clinicians want trustworthy automated decisions. One way to improve trust is to demonstr…
Classificationobject-detectionObject DetectionScene ClassificationAudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning
Previous studies in automated audio captioning have faced difficulties in accurately capturing the complete temporal details of acoustic scenes and events within long audio sequences. This paper presents AudioLog, a larg…
Acoustic Scene ClassificationAudio captioningContrastive LearningEvent Detection+3SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to…
Change DetectionmodelRepresentation LearningScene Classification+2CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision
The recent development of deep learning methods applied to vision has enabled their increasing integration into real-world applications to perform complex Computer Vision (CV) tasks. However, image acquisition conditions…
object-detectionObject DetectionScene ClassificationScene SegmentationUnlocking the capabilities of explainable fewshot learning in remote sensing
Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the appl…
Scene ClassificationAudio Event-Relational Graph Representation Learning for Acoustic Scene Classification
Most deep learning-based acoustic scene classification (ASC) approaches identify scenes based on acoustic features converted from audio clips containing mixed information entangled by polyphonic audio events (AEs). Howev…
Acoustic Scene ClassificationGraph Representation LearningRepresentation LearningScene ClassificationBringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification
The correlation between the sharpness of loss minima and generalisation in the context of deep neural networks has been subject to discussion for a long time. Whilst mostly investigated in the context of selected benchma…
Acoustic Scene ClassificationScene ClassificationLocality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs
We present a locality-aware method for interpreting the latent space of wavelet-based Generative Adversarial Networks (GANs), that can well capture the large spatial and spectral variability that is characteristic to sat…
Data AugmentationScene ClassificationMVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification
Vision Transformer (ViT) models have recently emerged as powerful and versatile models for various visual tasks. Recently, a work called PMF has achieved promising results in few-shot image classification by utilizing pr…
Data AugmentationDiversityDomain AdaptationFew-Shot Image Classification+5Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
The increasing availability of multi-sensor data sparks wide interest in multimodal self-supervised learning. However, most existing approaches learn only common representations across modalities while ignoring intra-mod…
Scene ClassificationSelf-Supervised LearningSemantic SegmentationSOAR: Scene-debiasing Open-set Action Recognition
Deep learning models have a risk of utilizing spurious clues to make predictions, such as recognizing actions based on the background scene. This issue can severely degrade the open-set action recognition performance whe…
Action RecognitionDecoderOpen Set Action RecognitionScene ClassificationUniversal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial examples poses a critical challenge, compromis…
Adversarial DefenseAdversarial PurificationDenoisingEarth Observation+2Visual Saliency Detection in Advanced Driver Assistance Systems
Visual Saliency refers to the innate human mechanism of focusing on and extracting important features from the observed environment. Recently, there has been a notable surge of interest in the field of automotive researc…
Photoplethysmography (PPG)Saliency DetectionScene ClassificationSemantic SegmentationDo humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type
Deep Learning models like Convolutional Neural Networks (CNN) are powerful image classifiers, but what factors determine whether they attend to similar image areas as humans do? While previous studies have focused on tec…
Explainable artificial intelligenceScene ClassificationEnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition
Scene recognition based on deep-learning has made significant progress, but there are still limitations in its performance due to challenges posed by inter-class similarities and intra-class dissimilarities. Furthermore,…
ClassificationEnsemble LearningScene ClassificationScene Recognition+1TbExplain: A Text-based Explanation Method for Scene Classification Models with the Statistical Prediction Correction
The field of Explainable Artificial Intelligence (XAI) aims to improve the interpretability of black-box machine learning models. Building a heatmap based on the importance value of input features is a popular method for…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Scene ClassificationIn-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification
We investigate the utility of in-domain self-supervised pre-training of vision models in the analysis of remote sensing imagery. Self-supervised learning (SSL) has emerged as a promising approach for remote sensing image…
Classificationimage-classificationImage ClassificationRemote Sensing Image Classification+2