no code implementations • 24 Jun 2019 • Xiaoting Wu, Eric Granger, Xiaoyi Feng
Then, early and late fusion methods are evaluated on the TALKIN dataset for the study of kinship verification with both face and voice modalities.
no code implementations • ACL 2020 • Yi Huang, Junlan Feng, Min Hu, Xiaoting Wu, Xiaoyu Du, Shuo Ma
The state-of-the-art accuracy for DST is below 50{\%} for a multi-domain dialogue task.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Yi Huang, Junlan Feng, Shuo Ma, Xiaoyu Du, Xiaoting Wu
In this paper, we propose a meta-learning based semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation, denoted as MEDST.
no code implementations • EANCS 2021 • Yi Huang, Junlan Feng, Xiaoting Wu, Xiaoyu Du
Our findings are: the performance variance of generative DSTs is not only due to the model structure itself, but can be attributed to the distribution of cross-domain values.
no code implementations • 12 Sep 2023 • Constantino Álvarez Casado, Manuel Lage Cañellas, Matteo Pedone, Xiaoting Wu, Le Nguyen, Miguel Bordallo López
For the first time, this work introduces regression models capable of estimating age and body mass index (BMI) based solely on acoustic data, as well as a model for sex classification.
no code implementations • 14 Sep 2023 • Xiaoting Wu, Xiaoyi Feng, Constantino Álvarez Casado, Lili Liu, Miguel Bordallo López
Specifically, in this paper, we employed a straightforward one-dimensional Convolutional Neural Network (1DCNN) with a 1DCNN-Attention module and kinship contrastive loss to learn the kin similarity from rPPGs.
no code implementations • 11 Dec 2023 • Le Ngu Nguyen, Praneeth Susarla, Anirban Mukherjee, Manuel Lage Cañellas, Constantino Álvarez Casado, Xiaoting Wu, Olli~Silvén, Dinesh Babu Jayagopi, Miguel Bordallo López
In this context, we present a comprehensive survey of multimodal approaches for indoor human monitoring systems, with a specific focus on their relevance in elderly care.