BAFN: Bi-Direction Attention Based Fusion Network for Multimodal Sentiment Analysis
Author:
Affiliation:
1. College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
2. Center for Advanced Intelligence Project, RIKEN, Saitama, Japan
3. Netease Fuxi AI Lab, NetEase, Hangzhou, China
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China for Intergovernmental International Science and Technology Innovation Cooperation Project
Key Research and Development Project of Zhejiang Province
JSPS KAKENHI
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Media Technology
Link
http://xplorestaging.ieee.org/ielx7/76/10091959/09932611.pdf?arnumber=9932611
Reference62 articles.
1. Fusing audio, visual and textual clues for sentiment analysis from multimodal content
2. Uses and abuses of the cross-entropy loss: Case studies in modern deep learning;gordon-rodriguez;arXiv 2011 05231,2020
3. Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis
4. CNN-Based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss
5. Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
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