| |
Records |
Links |
|
Author |
Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez |
|
| |
Title |
Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
ACM Transactions on Multimedia Computing, Communications, and Applications |
Abbreviated Journal |
ACMTMC |
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition |
|
| |
Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
|
| |
Address  |
May 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE; 600.157 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GYW2022 |
Serial |
3692 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez |
|
| |
Title |
Image rain removal and illumination enhancement done in one go |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
|
| |
Volume |
252 |
Issue |
|
Pages |
109244 |
|
| |
Keywords |
|
|
| |
Abstract |
Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement. |
|
| |
Address |
Sept 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE; 600.157; 600.168 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ WCS2022 |
Serial |
3744 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez |
|
| |
Title |
Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem |
Type |
Journal |
| |
Year |
2020 |
Publication |
Mathematics |
Abbreviated Journal |
MATH |
|
| |
Volume |
20 |
Issue |
8(9) |
Pages |
1595 |
|
| |
Keywords |
STEM education; Project-based learning; Coding; software tool |
|
| |
Abstract |
In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view. |
|
| |
Address |
September 2020 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
IAM; ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
3722 |
|
| Permanent link to this record |
