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Found 20 papers, 1 papers with code
AIT_FHSTP at GermEval 2021: Automatic Fact Claiming Detection with Multilingual Transformer Models
Our best model is GottBERT (i. e., a BERT transformer pre-trained on German texts) fine-tuned on the GermEval 2021 data.
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
In this paper, we propose a deep learning based model for Acoustic Anomaly Detection of Machines, the task for detecting abnormal machines by analysing the machine sound.
LSTM-based Deep Neural Network With A Focus on Sentence Representation for Sequential Sentence Classification in Medical Scientific Abstracts
For this reason, the role of sentence embedding is crucial for capturing both the semantic information between words in the sentence and the contextual relationship of sentences within the abstract to provide a comprehensive representation for better classification.
Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network
Finally, we propose a combined loss function which leverages Focal loss and IoU loss to train the network.
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms
In the second phase, the student network, which presents a low complexity model, is trained with the embeddings extracted from the teacher.
A Light-weight Deep Learning Model for Remote Sensing Image Classification
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image.
Robust, 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.
Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network
The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has taken the important role in a wide range of applications such as urban planning, natural hazards detection, environment monitoring, vegetation mapping, or geospatial object detection.
Low-complexity deep learning frameworks for acoustic scene classification
In this report, we presents low-complexity deep learning frameworks for acoustic scene classification (ASC).
Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices
In this paper, we present a robust and low complexity system for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording.
An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification
This paper presents a task of audio-visual scene classification (SC) where input videos are classified into one of five real-life crowded scenes: 'Riot', 'Noise-Street', 'Firework-Event', 'Music-Event', and 'Sport-Atmosphere'.
Automatic Sexism Detection with Multilingual Transformer Models
For both tasks our best model is XLM-R with unsupervised pre-training on the EXIST data and additional datasets and fine-tuning on the provided dataset.
Multi-Modal Video Forensic Platform for Investigating Post-Terrorist Attack Scenarios
Specifically, Audio Event Detection is applied to index the content according to attack-specific acoustic concepts.
Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text
We present an approach to unsupervised audio representation learning.
Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis
In a further consequence it can be concluded that this visual information is music related and thus should be beneficial for the corresponding MIR tasks such as music genre classification or mood recognition.
Deep Learning for MIR Tutorial
Deep Learning has become state of the art in visual computing and continuously emerges into the Music Information Retrieval (MIR) and audio retrieval domain.
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry.
Large Scale Audio-Visual Video Analytics Platform for Forensic Investigations of Terroristic Attacks
The heterogeneous results of the analytical modules are fused into a distributed index of visual and acoustic concepts to facilitate rapid start of investigations, following traits and investigating witness reports.
Fashion and Apparel Classification using Convolutional Neural Networks
We present an empirical study of applying deep Convolutional Neural Networks (CNN) to the task of fashion and apparel image classification to improve meta-data enrichment of e-commerce applications.
A Multi-modal Deep Neural Network approach to Bird-song identification
The audio is fed into a Convolutional Neural Network (CNN) using four convolutional layers.
Sound Audio and Speech Processing
