https://github.com/yangxiaocui1215/multipoint
공식 코드Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts
Papers with Code 아카이브
Multimodal Sentiment Analysis
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Multimodal sentiment analysis is the task of performing sentiment analysis with multiple data sources - e.g. a camera feed of someone's face and their recorded speech.
<span style="color:grey; opacity: 0.6">( Image credit: ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yaohungt/Multimodal-Transformer
공식 코드Multimodal Transformer for Unaligned Multimodal Language Sequences
https://github.com/zehuiwu/MMML
공식 코드https://github.com/zinengtang/tvlt
공식 코드https://github.com/zrguo/MPLMM
공식 코드Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition
https://github.com/zrguo/casp
공식 코드Bridging the Gap for Test-Time Multimodal Sentiment Analysis
https://github.com/18XiWenjuan/LMF_Paddle
Efficient Low-rank Multimodal Fusion with Modality-Specific Factors
https://github.com/Avaneesh-S/Tensor-Fusion-Network
https://github.com/JhnLee/multimodal-transformer
Multimodal Transformer for Unaligned Multimodal Language Sequences
https://github.com/Justin1904/TensorFusionNetworks
https://github.com/MagnusXu/Speech-Emotion-Recognition-Capstone-Project
https://github.com/MindCode-4/code-1/tree/main/tvlt
https://github.com/MindCode-4/code-5/tree/main/tvlt
https://github.com/SER-2020-Project-ZX/Reference
https://github.com/SenticNet/hfusion
Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling
https://github.com/aris-ai/Audio-and-text-based-emotion-recognition
https://github.com/clin366/MFN
Multi-attention Recurrent Network for Human Communication Comprehension
https://github.com/cmu-multicomp-lab/cmu-multimodalsdk
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
https://github.com/david-yoon/multimodal-speech-emotion
https://github.com/declare-lab/BBFN
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis
https://github.com/jbdel/MOSEI_UMONS
A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis
https://github.com/maochf/CMU-MultimodalSDK
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
https://github.com/matsuolab/cmu-multimodalsdk
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
https://github.com/pliang279/MFN
Multi-attention Recurrent Network for Human Communication Comprehension
https://github.com/righ120/multimodal_nlp
Efficient Low-rank Multimodal Fusion with Modality-Specific Factors
https://github.com/robertjkeck2/EmoNet
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
https://github.com/robertjkeck2/EmoTe
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
https://github.com/robertjkeck2/pwdl
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
https://github.com/senticnet/sc-lstm
https://github.com/soujanyaporia/contextual-multimodal-fusion
https://github.com/soujanyaporia/multimodal-sentiment-analysis
https://github.com/thuiar/Cross-Modal-BERT
https://github.com/tjdevWorks/TEASEL
https://github.com/yc-li20/SER-on-WER-and-Fusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.