Automatic Sign Language Translation (SLT) systems can be a great asset to improve the communication with and within deaf communities. Currently, the main issue preventing effective translation models lays in the low availability of labelled data, which hinders the use of modern deep learning models. SLT is a complex problem that involves many subtasks, of which handshape recognition is the most important. We compare a series of models specially tailored for small datasets to improve their performance on handshape recognition tasks. We evaluate Wide-DenseNet and few-shot Prototypical Network models with and without transfer learning, and also using Model-Agnostic Meta-Learning (MAML). Our findings indicate that Wide-DenseNet without transfer learning and Prototipical Networks with transfer learning provide the best results. Prototypical networks, particularly, are vastly superior when using less than 30 samples, while Wide-DenseNet achieves the best results with more samples. On the other hand, MAML does not improve performance in any scenario. These results can help to design better SLT models.
The Web is a vast source of semi-structured data sets that are made readily available to support the construction of new knowledge. Information visualization techniques have been demonstrated a suitable alternative for allowing users to analyze and understand a large amount of data. However, the steps required for visualizing semi-structured data obtained from the Web is not straightforward, and it requires proper treatment before information visualization techniques could be applied. In this work, we present a visualization pipeline for describing the fundamental operations required for visualizing semi-structured data over the Web. For that, we employ Web Scrapping and Web Augmentation techniques for supporting interactive visualizations and solving tasks without changing the context of use of the data. Our approach is duly supported by a framework including scrapping, augmenting and visualization tools and it has been applied to different kinds of websites to demonstrate its validity and feasibility. Our ultimate goal is to expand the limits of our technology for improving the user interaction with websites and creating new experiences for better understanding large data sets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.