|
|
|
|
|
Peranut Nimitsurachat and Peter Washington
Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich, a m...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xiaodong Cui, Zhuofan He, Yangtao Xue, Keke Tang, Peican Zhu and Jing Han
Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot le...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yusuf Brima, Ulf Krumnack, Simone Pika and Gunther Heidemann
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yan Wang, Nan Guan, Jie Li and Xiaoli Wang
Fourier ptychographic microscopy (FPM) is a computational imaging technology that has endless vitality and application potential in digital pathology. Colored pathological image analysis is the foundation of clinical diagnosis, basic research, and most b...
ver más
|
|
|
|
|
|
|
|
|
|
|
Nils Hütten, Miguel Alves Gomes, Florian Hölken, Karlo Andricevic, Richard Meyes and Tobias Meisen
Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is ther...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zitong Yan, Hongmei Liu, Laifa Tao, Jian Ma and Yujie Cheng
To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Padraig Corcoran and Irena Spasic
Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine lear...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xueliang Wang, Nan Yang, Enjun Liu, Wencheng Gu, Jinglin Zhang, Shuo Zhao, Guijiang Sun and Jian Wang
In order to solve the problem of manual labeling in semi-supervised tree species classification, this paper proposes a pixel-level self-supervised learning model named M-SSL (multisource self-supervised learning), which takes the advantage of the informa...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xuefeng Zhang, Youngsung Kim, Young-Chul Chung, Sangcheol Yoon, Sang-Yong Rhee and Yong Soo Kim
Large-scale datasets, which have sufficient and identical quantities of data in each class, are the main factor in the success of deep-learning-based classification models for vision tasks. A shortage of sufficient data and interclass imbalanced data dis...
ver más
|
|
|
|
|
|
|
|
|
|
|
Tanvir Islam and Peter Washington
Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interv...
ver más
|
|
|
|
|
|