Papers Scene Classification
“Scene Classification” 태그가 달린 논문 497편 · 필터 해제
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene Classification
Most existing deep learning-based acoustic scene classification (ASC) approaches directly utilize representations extracted from spectrograms to identify target scenes. However, these approaches pay little attention to t…
Acoustic Scene ClassificationGraph Representation LearningRepresentation LearningScene ClassificationEfficient Similarity-based Passive Filter Pruning for Compressing CNNs
Convolution neural networks (CNNs) have shown great success in various applications. However, the computational complexity and memory storage of CNNs is a bottleneck for their deployment on resource-constrained devices. …
Acoustic Scene ClassificationScene ClassificationMulti-Source Transformer Architectures for Audiovisual Scene Classification
In this technical report, the systems we submitted for subtask 1B of the DCASE 2021 challenge, regarding audiovisual scene classification, are described in detail. They are essentially multi-source transformers employing…
ClassificationScene ClassificationRobust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context
In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. In particular, we firstly propose an incepti…
Acoustic Scene ClassificationScene ClassificationWaveMix-Lite: A Resource-efficient Neural Network for Image Analysis
Gains in the ability to generalize on image analysis tasks for neural networks have come at the cost of increased number of parameters and layers, dataset sizes, training and test computations, and GPU RAM. We introduce …
Efficient Neural NetworkGPUimage-classificationImage Classification+2A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
The traditional Machine Learning (ML) methodology requires to fragment the development and experimental process into disconnected iterations whose feedback is used to guide design or tuning choices. This methodology has …
Domain GeneralizationFine-Grained Image Classificationimage-classificationImage Classification+3Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification
Sound event detection (SED) and Acoustic scene classification (ASC) are two widely researched audio tasks that constitute an important part of research on acoustic scene analysis. Considering shared information between s…
Acoustic Scene ClassificationEvent DetectionScene ClassificationSound Event DetectionLeveraging Large (Visual) Language Models for Robot 3D Scene Understanding
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of an average human, we investigate the use …
Common Sense ReasoningScene ClassificationScene UnderstandingAll grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification
Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly and 2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how …
Aerial Scene ClassificationAllMultiple Instance LearningScene ClassificationMinimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning
Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns. We tackle these challenges through utilizing the framework of deep …
Depth Estimationimage-classificationImage ClassificationScene ClassificationLow-complexity CNNs for Acoustic Scene Classification
This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC framework should have maximum 128K para…
Acoustic Scene ClassificationClassificationScene ClassificationLow-complexity CNNs for Acoustic Scene Classification
This paper presents a low-complexity framework for acoustic scene classification (ASC). Most of the frameworks designed for ASC use convolutional neural networks (CNNs) due to their learning ability and improved performa…
Acoustic Scene ClassificationClassificationQuantizationScene ClassificationScene Recognition with Objectness, Attribute and Category Learning
Scene classification has established itself as a challenging research problem. Compared to images of individual objects, scene images could be much more semantically complex and abstract. Their difference mainly lies in …
AttributeScene ClassificationScene RecognitionSeasoNet: A Seasonal Scene Classification, segmentation and Retrieval dataset for satellite Imagery over Germany
This work presents SeasoNet, a new large-scale multi-label land cover and land use scene understanding dataset. It includes $1\,759\,830$ images from Sentinel-2 tiles, with 12 spectral bands and patch sizes of up to $ 12…
Image RetrievalRetrievalScene ClassificationScene Understanding+1Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general sc…
Instance SegmentationPanoptic SegmentationScene ClassificationScene Classification (unified classes)+3Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain
Currently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning. …
Land Cover Classificationobject-detectionObject DetectionScene Classification+1$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features
In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in $l_2$ norms of sample features can hinder batch normalizati…
Acoustic Scene Classificationimage-classificationImage ClassificationScene ClassificationFew-Shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext Tasks
Small data hold big artificial intelligence (AI) potential. As one of the promising small data AI approaches, few-shot learning has the goal to learn a model efficiently that can recognize novel classes with extremely li…
Contrastive LearningFew-Shot LearningScene ClassificationCrime scene classification from skeletal trajectory analysis in surveillance settings
Video anomaly analysis is a core task actively pursued in the field of computer vision, with applications extending to real-world crime detection in surveillance footage. In this work, we address the task of human-relate…
ClassificationCrime PredictionScene ClassificationQTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design
This technical report describes the details of our TASK1A submission of the DCASE2021 challenge. The goal of the task is to design an audio scene classification system for device-imbalanced datasets under the constraints…
Acoustic Scene ClassificationKnowledge DistillationModel CompressionQuantization+1