Papers Scene Classification
“Scene Classification” 태그가 달린 논문 497편 · 필터 해제
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers
Varying conditions between the data seen at training and at application time remain a major challenge for machine learning. We study this problem in the context of Acoustic Scene Classification (ASC) with mismatching rec…
Acoustic Scene ClassificationScene ClassificationDomain Information Control at Inference Time for Acoustic Scene Classification
Domain shift is considered a challenge in machine learning as it causes significant degradation of model performance. In the Acoustic Scene Classification task (ASC), domain shift is mainly caused by different recording …
Acoustic Scene ClassificationDomain GeneralizationScene ClassificationAcoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration
Recent efforts have been made on acoustic scene classification in the audio signal processing community. In contrast, few studies have been conducted on acoustic scene clustering, which is a newly emerging problem. Acous…
Acoustic Scene ClassificationAudio Signal ProcessingClusteringScene ClassificationEfficient Multi-Task Scene Analysis with RGB-D Transformers
Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the scene requires solving multiple tasks, su…
Panoptic SegmentationScene ClassificationSemantic SegmentationLow-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet
We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on classifying audio samples of multiple devi…
Acoustic Scene ClassificationData AugmentationModel CompressionScene ClassificationMulti-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image Scenes
Zero-shot classification of image scenes which can recognize the image scenes that are not seen in the training stage holds great promise of lowering the dependence on large numbers of labeled samples. To address the zer…
ClassificationContrastive LearningScene Classificationzero-shot-classification+1DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes
Lifelong audio feature extraction involves learning new sound classes incrementally, which is essential for adapting to new data distributions over time. However, optimizing the model only on new data can lead to catastr…
Acoustic Scene ClassificationContinual LearningQuantizationRepresentation Learning+1Human-annotated label noise and their impact on ConvNets for remote sensing image scene classification
Convolutional neural networks (ConvNets) have been successfully applied to satellite image scene classification. Human-labeled training datasets are essential for ConvNets to perform accurate classification. Errors in hu…
Scene ClassificationLow-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms
In this technical report, a low-complexity deep learning system for acoustic scene classification (ASC) is presented. The proposed system comprises two main phases: (Phase I) Training a teacher network; and (Phase II) tr…
Acoustic Scene ClassificationScene ClassificationDevice-Robust Acoustic Scene Classification via Impulse Response Augmentation
The ability to generalize to a wide range of recording devices is a crucial performance factor for audio classification models. The characteristics of different types of microphones introduce distributional shifts in the…
Acoustic Scene ClassificationAudio ClassificationClassificationDiversity+1Vision-Language Models in Remote Sensing: Current Progress and Future Trends
The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These models provide intelligent solutions cl…
Image CaptioningImage GenerationImage Retrievalobject-detection+6Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit
Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine learning model on a satellite constella…
Federated LearningScene ClassificationCompressing audio CNNs with graph centrality based filter pruning
Convolutional neural networks (CNNs) are commonplace in high-performing solutions to many real-world problems, such as audio classification. CNNs have many parameters and filters, with some having a larger impact on the …
Acoustic Scene ClassificationAudio ClassificationAudio TaggingScene ClassificationUnsupervised Improvement of Audio-Text Cross-Modal Representations
Recent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional training approaches that use predefined labels. This has allowed the community to ma…
Acoustic Scene ClassificationClassificationScene Classificationzero-shot-classification+1Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification
Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems. Recently, the existing deep learning (DL)-based met…
ClassificationRobust classificationScene ClassificationWATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's a…
ClassificationComputational Efficiencyimage-classificationImage Classification+2CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding
Self-supervised learning (SSL) has gained widespread attention in the remote sensing (RS) and earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels.…
Change DetectionContrastive LearningEarth Observationobject-detection+5On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-sho…
Few-Shot LearningScene ClassificationTime Series ForecastingToponym Recognition+1APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP
In recent years, the success of large-scale vision-language models (VLMs) such as CLIP has led to their increased usage in various computer vision tasks. These models enable zero-shot inference through carefully crafted …
Domain GeneralizationPrompt LearningScene ClassificationEfficient CNNs via Passive Filter Pruning
Convolutional neural networks (CNNs) have shown state-of-the-art performance in various applications. However, CNNs are resource-hungry due to their requirement of high computational complexity and memory storage. Recent…
Computational Efficiencyimage-classificationImage ClassificationScene Classification