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SOTA
Multimodal Sentiment Analysis
Multimodal Sentiment Analysis On Cmu Mosi
Multimodal Sentiment Analysis On Cmu Mosi
評価指標
Acc-2
Acc-7
Corr
F1
MAE
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Acc-2
Acc-7
Corr
F1
MAE
Paper Title
Repository
TEASEL
87.5
47.52
0.836
85
0.64
TEASEL: A Transformer-Based Speech-Prefixed Language Model
self-M
82.54
45.79
0.795
82.68
0.712
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis
SPECTRA
87.5
-
-
-
-
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
UniVL + MELTR
85.3
-
0.789
85.4
0.759
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Tri-TransModality
-
-
-
-
-
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis
-
MMML
90.35
52.72
0.8824
90.35
0.5573
Multimodal Multi-loss Fusion Network for Sentiment Analysis
MMIM
84.14
46.65
0.8
84
0.7
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis
MAG-BERT*
82.37
43.62
0.781
82.5
0.727
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis
UniMSE
86.9
48.68
0.809
86.42
0.691
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
ALMT
-
49.42
0.805
-
0.683
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis
MCEN
87.35
50.58
0.813
87.48
0.678
10,000+ Times Accelerated Robust Subset Selection (ARSS)
-
VAE-AMDT
84.3
-
-
84.2
0.716
Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis
-
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