no code implementations • 15 Mar 2024 • Jin Cui, Fumiyo Fukumoto, Xinfeng Wang, Yoshimi Suzuki, Jiyi Li, Noriko Tomuro, Wanzeng Kong
To address the issue of multiple aspect categories and sentiment entanglement, we propose a hierarchical disentanglement module to extract distinct categories and sentiment features.
1 code implementation • 15 Dec 2023 • Yuhang Ming, Jian Ma, Xingrui Yang, Weichen Dai, Yong Peng, Wanzeng Kong
We evaluate our AEGIS-Net on the ScanNetPR dataset and compare its performance with a pre-deep-learning feature-based method and five state-of-the-art deep-learning-based methods.
no code implementations • 20 Dec 2022 • Feng Qiu, Wanzeng Kong, Yu Ding
Humans are sophisticated at reading interlocutors' emotions from multimodal signals, such as speech contents, voice tones and facial expressions.
no code implementations • 16 Dec 2022 • Feng Qiu, Chengyang Xie, Yu Ding, Wanzeng Kong
In this paper, we design three kinds of multimodal latent representations to refine the emotion analysis process and capture complex multimodal interactions from different views, including a intact three-modal integrating representation, a modality-shared representation, and three modality-individual representations.
no code implementations • ACL 2021 • Jiajia Tang, Kang Li, Xuanyu Jin, Andrzej Cichocki, Qibin Zhao, Wanzeng Kong
In this work, the coupled-translation fusion network (CTFN) is firstly proposed to model bi-direction interplay via couple learning, ensuring the robustness in respect to missing modalities.
no code implementations • 3 Nov 2020 • Guang Lin, Jianhai Zhang, Yuxi Liu, Tianyang Gao, Wanzeng Kong, Xu Lei, Tao Qiu
Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science.
1 code implementation • 3 Jul 2020 • Dongrui Wu, Xue Jiang, Ruimin Peng, Wanzeng Kong, Jian Huang, Zhigang Zeng
Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance.
no code implementations • NeurIPS 2019 • Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, Qibin Zhao
Tensor-based multimodal fusion techniques have exhibited great predictive performance.