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Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various human emotions from heterogeneous visual, audio and text modalities.
Learning multi-label scene classification
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Multimodal deep learning
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COVAREP—A collaborative voice analysis repository for speech technologies
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Generative adversarial nets
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Glove: Global vectors for word representation
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A Review on Multi-Label Learning Algorithms
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Attention is all you need
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Tensor Fusion Network for Multimodal Sentiment Analysis
Zadeh, A.; Chen, M.; Poria, S.; Cambria, E.; and Morency, L.-P. 2017 · 2017
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Efficient Low-rank Multimodal Fusion With Modality-Specific Factors
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Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis
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Learning Factorized Multimodal Representations
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SGM: Sequence Generation Model for Multi-label Classification
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Label-specific document representation for multi-label text classification
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Self-Supervised MultiModal Versatile Networks
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Misa: Modality-invariant and-specific representations for multimodal sentiment analysis
Hazarika, D.; Zimmermann, R.; Poria; et al. 2020 · 2020
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Real-time emotion recognition via attention gated hierarchical memory network
Jiao, W.; Lyu, M.; and King, I. 2020 · 2020
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Transformer-based label set generation for multi-modal multi-label emotion detection
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Federated learning for vision-and-language grounding problems
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Latent semantic aware multi-view multi-label classification
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Multi-Label Learning with Global and Local Label Correlation
Zhu, Y.; Kwok, J. T.; and Zhou, Z.-H. 2018 · 2018
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Multimodal Machine Learning: A Survey and Taxonomy
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Collaboration based multi-label learning
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Dialoguernn: An attentive rnn for emotion detection in conversations
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Liu, F.; Wu, X.; Ge, S.; Fan, W.; and Zou, Y. 2020 · 2020
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Modality to modality translation: An adversarial representation learning and graph fusion network for multimodal fusion
Mai, S.; Hu, H.; and Xing, S. 2020 · 2020
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Towards emotion-aided multi-modal dialogue act classification
Saha, T.; Patra, A.; Saha, S.; and Bhattacharyya, P. 2020 · 2020
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Learning relationships between text, audio, and video via deep canonical correlation for multimodal language analysis
Sun, Z.; Sarma, P.; Sethares, W.; and Liang, Y. 2020 · 2020
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Self-supervised model adaptation for multimodal semantic segmentation
Valada, A.; Mohan, R.; and Burgard, W. 2020 · 2020
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Ch-sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality
Yu, W.; Xu, H.; Meng, F.; Zhu, Y.; Ma, Y.; Wu, J.; Zou, J.; and Yang, K. 2020 · 2020
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An End-to-End visual-audio attention network for emotion recognition in user-generated videos
Zhao, S.; Ma, Y.; Gu, Y.; Yang, J.; Xing, T.; Xu, P.; Hu, R.; Chai, H.; and Keutzer, K. 2020 · 2020
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Audio-Oriented Multimodal Machine Comprehension via Dynamic Inter-and Intra-modality Attention
Huang, Z.; Liu, F.; Wu, X.; Ge, S.; Wang, H.; Fan, W.; and Zou, Y. 2021 · 2021
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Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences
Lv, F.; Chen, X.; Huang, Y.; Duan, L.; and Lin, G. 2021 · 2021
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BiLabel-Specific Features for Multi-Label Classification
Zhang, M.-L.; Fang, J.-P.; and Wang, Y.-B. 2021 · 2021
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