Papers Scene Classification
“Scene Classification” 태그가 달린 논문 497편 · 필터 해제
Multi-Modal RGB-D Scene Recognition Across Domains
Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that complement the RGB texture information …
Scene ClassificationScene RecognitionMSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels
Supervised learning techniques are at the center of many tasks in remote sensing. Unfortunately, these methods, especially recent deep learning methods, often require large amounts of labeled data for training. Even thou…
ClassificationGeneral ClassificationImage ClassificationScene ClassificationScene Retrieval for Contextual Visual Mapping
Visual navigation localizes a query place image against a reference database of place images, also known as a `visual map'. Localization accuracy requirements for specific areas of the visual map, `scene classes', vary a…
General ClassificationImage RetrievalRetrievalScene Classification+3Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech
Our ability to comprehend speech remains, to date, unrivaled by deep learning models. This feat could result from the brain's ability to fine-tune generic sound representations for speech-specific processes. To test this…
Scene ClassificationSpeech-to-TextDeep Learning for Scene Classification: A Survey
Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision. The rise o…
ClassificationDeep LearningGeneral ClassificationScene Classification+1HexCNN: A Framework for Native Hexagonal Convolutional Neural Networks
Hexagonal CNN models have shown superior performance in applications such as IACT data analysis and aerial scene classification due to their better rotation symmetry and reduced anisotropy. In order to realize hexagonal …
Aerial Scene ClassificationScene ClassificationLearning Visual Representation from Human Interactions
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as ima…
Action RecognitionDepth EstimationRepresentation LearningScene ClassificationMGML: Multi-Granularity Multi-Level Feature Ensemble Network for Remote Sensing Scene Classification
Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main characters: large intra-class variance caused by large resolution variance and confusing in…
ClassificationEnsemble LearningGeneral ClassificationScene ClassificationLow-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping
Deep Neural Networks are known to be very demanding in terms of computing and memory requirements. Due to the ever increasing use of embedded systems and mobile devices with a limited resource budget, designing low-compl…
Acoustic Scene ClassificationScene ClassificationA Two-Stage Approach to Device-Robust Acoustic Scene Classification
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is propos…
Acoustic Scene ClassificationClassificationData AugmentationGeneral Classification+2ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles
Advanced video analytic systems, including scene classification and object detection, have seen widespread success in various domains such as smart cities and autonomous transportation. With an ever-growing number of pow…
Objectobject-detectionObject DetectionObject Tracking+3Exploiting Context for Robustness to Label Noise in Active Learning
Several works in computer vision have demonstrated the effectiveness of active learning for adapting the recognition model when new unlabeled data becomes available. Most of these works consider that labels obtained from…
Active LearningDocument ClassificationGeneral ClassificationScene ClassificationWhat Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as ima…
Action RecognitionDepth EstimationRepresentation LearningScene ClassificationRemote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples
With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification. However, supervised learning requires a huge number of annotated data for training. Wh…
ClassificationGeneral ClassificationScene ClassificationSelf-Supervised LearningMLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding
To better understand scene images in the field of remote sensing, multi-label annotation of scene images is necessary. Moreover, to enhance the performance of deep learning models for dealing with semantic scene understa…
Deep Learningimage-classificationImage ClassificationImage Retrieval+3A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification
This paper analyzes and compares different deep learning loss functions in the framework of multi-label remote sensing (RS) image scene classification problems. We consider seven loss functions: 1) cross-entropy loss; 2)…
General Classificationimage-classificationImage ClassificationRemote Sensing Image Classification+1RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification
Training a modern deep neural network on massive labeled samples is the main paradigm in solving the scene classification problem for remote sensing, but learning from only a few data points remains a challenge. Existing…
General ClassificationMetric LearningScene Classification2D-3D Geometric Fusion Network using Multi-Neighbourhood Graph Convolution for RGB-D Indoor Scene Classification
Multi-modal fusion has been proved to help enhance the performance of scene classification tasks. This paper presents a 2D-3D Fusion stage that combines 3D Geometric Features with 2D Texture Features obtained by 2D Convo…
General ClassificationScene ClassificationDCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events
Single task deep neural networks that perform a target task among diverse cross-related tasks in the acoustic scene and event literature are being developed. Few studies exist that investigate to combine such tasks, howe…
Acoustic Scene ClassificationAudio TaggingEvent DetectionScene Classification+1Deriving Visual Semantics from Spatial Context: An Adaptation of LSA and Word2Vec to generate Object and Scene Embeddings from Images
Embeddings are an important tool for the representation of word meaning. Their effectiveness rests on the distributional hypothesis: words that occur in the same context carry similar semantic information. Here, we adapt…
ObjectScene Classification