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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/kenford953/graphcage
공식 코드Graph Capsule Aggregation for Unaligned Multimodal Sequences
https://github.com/kiva12138/CubeMLP
공식 코드CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation
https://github.com/leduckhai/multimed
공식 코드https://github.com/leduckhai/sentiment-reasoning
공식 코드https://github.com/lemei/affective-computing
공식 코드Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
https://github.com/lemei/unimse
공식 코드UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
https://github.com/liaolea/emoverse
공식 코드EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding
https://github.com/lijfrank-open/GraphSmile
공식 코드https://github.com/lstappen/MuSe-Toolbox
공식 코드https://github.com/lstappen/MuSe2020
공식 코드https://github.com/lstappen/MuSe2021
공식 코드https://github.com/mahuanaaa/monig
공식 코드Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
https://github.com/mlvlab/MELTR
공식 코드MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
https://github.com/ouyangkun10/edge
공식 코드Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue
https://github.com/pwang322/dlf
공식 코드DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis
https://github.com/recklessronan/muse
공식 코드https://github.com/rongfei-chen/VCAN
공식 코드Video-based Cross-modal Auxiliary Network for Multimodal Sentiment Analysis
https://github.com/shamanez/BERT-like-is-All-You-Need
공식 코드https://github.com/smwanghhh/co-sa
공식 코드Cooperative Sentiment Agents for Multimodal Sentiment Analysis
https://github.com/smwanghhh/mmcl
공식 코드Multi-Modality Collaborative Learning for Sentiment Analysis
https://github.com/sunlicai/emt-dlfr
공식 코드https://github.com/sverma88/Deep-HOSeq--ICDM-2020
공식 코드Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis
https://github.com/teng-sun/clue_model
공식 코드Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis
https://github.com/thuiar/M-SENA
공식 코드M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
https://github.com/thuiar/MMSA
공식 코드M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
https://github.com/thuiar/Self-MM
공식 코드https://github.com/thuiar/ch-sims-v2
공식 코드Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module
https://github.com/thuiar/mmsa-fet
공식 코드M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
https://github.com/tmacmai/multimodal-information-bottleneck
공식 코드https://github.com/victorywys/RAVEN
공식 코드Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
https://github.com/warmcongee/sdumc
공식 코드https://github.com/wsxrhfg/dbf
공식 코드Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion
https://github.com/x-g-y/sati
공식 코드Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning
https://github.com/xia-code/textual-modality-description
공식 코드https://github.com/xiaoyaoxinyi/m2se
공식 코드EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.