3,358 research outputs found
IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer
Recently, the contrastive learning paradigm has achieved remarkable success
in high-level tasks such as classification, detection, and segmentation.
However, contrastive learning applied in low-level tasks, like image
restoration, is limited, and its effectiveness is uncertain. This raises a
question: Why does the contrastive learning paradigm not yield satisfactory
results in image restoration? In this paper, we conduct in-depth analyses and
propose three guidelines to address the above question. In addition, inspired
by style transfer and based on contrastive learning, we propose a novel module
for image restoration called \textbf{ConStyle}, which can be efficiently
integrated into any U-Net structure network. By leveraging the flexibility of
ConStyle, we develop a \textbf{general restoration network} for image
restoration. ConStyle and the general restoration network together form an
image restoration framework, namely \textbf{IRConStyle}. To demonstrate the
capability and compatibility of ConStyle, we replace the general restoration
network with transformer-based, CNN-based, and MLP-based networks,
respectively. We perform extensive experiments on various image restoration
tasks, including denoising, deblurring, deraining, and dehazing. The results on
19 benchmarks demonstrate that ConStyle can be integrated with any U-Net-based
network and significantly enhance performance. For instance, ConStyle NAFNet
significantly outperforms the original NAFNet on SOTS outdoor (dehazing) and
Rain100H (deraining) datasets, with PSNR improvements of 4.16 dB and 3.58 dB
with 85% fewer parameters
CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds
Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being swift, low-cost, and eco-friendly. However, prior studies mainly focused on small-scale COVID-19 datasets. To build a robust model, the large-scale multi-sound FluSense dataset is utilised to help detect COVID-19 from cough sounds in this study. Due to the gap between FluSense and the COVID-19-related datasets consisting of cough only, the transfer learning framework (namely CovNet) is proposed and applied rather than simply augmenting the training data with FluSense. The CovNet contains (i) a parameter transferring strategy and (ii) an embedding incorporation strategy. Specifically, to validate the CovNet's effectiveness, it is used to transfer knowledge from FluSense to COUGHVID, a large-scale cough sound database of COVID-19 negative and COVID-19 positive individuals. The trained model on FluSense and COUGHVID is further applied under the CovNet to another two small-scale cough datasets for COVID-19 detection, the COVID-19 cough sub-challenge (CCS) database in the INTERSPEECH Computational Paralinguistics challengE (ComParE) challenge and the DiCOVA Track-1 database. By training four simple convolutional neural networks (CNNs) in the transfer learning framework, our approach achieves an absolute improvement of 3.57% over the baseline of DiCOVA Track-1 validation of the area under the receiver operating characteristic curve (ROC AUC) and an absolute improvement of 1.73% over the baseline of ComParE CCS test unweighted average recall (UAR). Copyright © 2022 Chang, Jing, Ren and Schuller
- …