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Found 13 papers, 6 papers with code
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data
Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data.
Self-supervised Sparse Representation for Video Anomaly Detection
Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence.
Anomaly Detection In Surveillance Videos
Self-Supervised Learning
Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features
In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.
STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model
To overcome this limitation, we propose a deep learning-based non-intrusive speech intelligibility assessment model, namely STOI-Net.
A Study on Incorporating Whisper for Robust Speech Assessment
The first part of this study investigates the correlation between the embedding features of Whisper and two self-supervised learning (SSL) models with subjective quality and intelligibility scores.
Speech Enhancement with Zero-Shot Model Selection
Experimental results confirmed that the proposed ZMOS approach can achieve better performance in both seen and unseen noise types compared to the baseline systems and other model selection systems, which indicates the effectiveness of the proposed approach in providing robust SE performance.
Dimensionality Reduction for Data in Multiple Feature Representations
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
Learning Unsupervised Metaformer for Anomaly Detection
Anomaly detection (AD) aims to address the task of classification or localization of image anomalies.
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model
Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention.
MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
In this study, we propose a multi-branched speech intelligibility prediction model (MBI-Net), for predicting the subjective intelligibility scores of HA users.
Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model
This study proposes a multi-task pseudo-label learning (MPL)-based non-intrusive speech quality assessment model called MTQ-Net.
Utilizing Whisper to Enhance Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
Automated assessment of speech intelligibility in hearing aid (HA) devices is of great importance.
