Sentiment Analysis on CMU-MOSEI

Metric: Accuracy (higher is better)

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	SeMUL-PCD	88.62	No	-	-	-
2	MMML	88.22	No	Multimodal Multi-loss Fusion Network for Sentime...	2023-08-01	Code
3	UniMSE	87.5	Yes	UniMSE: Towards Unified Multimodal Sentiment Ana...	2022-11-21	Code
4	SPECTRA	87.34	No	Speech-Text Dialog Pre-training for Spoken Dialo...	2023-05-19	Code
5	Transformer-based joint-encoding	82.48	Yes	A Transformer-based joint-encoding for Emotion R...	2020-06-29	Code
6	Modulated-fusion transformer	82.45	Yes	Modulated Fusion using Transformer for Linguisti...	2020-10-05	Code
7	MMLatch	82.4	Yes	MMLatch: Bottom-up Top-down Fusion for Multimoda...	2022-01-24	Code
8	Multilogue-Net	82.1	Yes	Multilogue-Net: A Context Aware RNN for Multi-mo...	2020-02-19	Code
9	Proposed: B2 + B4 w/ multimodal fusion	81.14	Yes	Gated Mechanism for Attention Based Multimodal S...	2020-02-21	-
10	CAE-LR	78	Yes	Unsupervised Multimodal Language Representations...	2021-10-06	-
11	Graph-MFN	76.9	Yes	-	-	-
12	MARLIN (ViT-L)	74.83	No	MARLIN: Masked Autoencoder for facial video Repr...	2022-11-12	Code
13	MARLIN (ViT-B)	73.7	No	MARLIN: Masked Autoencoder for facial video Repr...	2022-11-12	Code
14	MARLIN (ViT-S)	72.69	No	MARLIN: Masked Autoencoder for facial video Repr...	2022-11-12	Code