Papers with Code 아카이브

Multimodal Emotion Recognition

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 1
평가표: 7
보존된 평가 기록: 20

This is a leaderboard for multimodal emotion recognition on the IEMOCAP dataset. The modality abbreviations are A: Acoustic T: Text V: Visual

Please include the modality in the bracket after the model name.

All models must use standard five emotion categories and are evaluated in standard leave-one-session-out (LOSO). See the papers for references.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/201983290498/lddu_mmer

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Latent Distribution Decoupling: A Probabilistic Framework for Uncertainty-Aware Multimodal Emotion Recognition

https://github.com/ASolitaryMan/Foal-Net

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Enhancing Modal Fusion by Alignment and Label Matching for Multimodal Emotion Recognition

https://github.com/Peihao-Xiang/MultiMAE-DFER

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MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition

https://github.com/PoloWlg/Joint-Multimodal-Transformer-6th-ABAW

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Joint Multimodal Transformer for Emotion Recognition in the Wild

https://github.com/SenticNet/conv-emotion

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DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

https://github.com/amaai-lab/muvi

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Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses

https://github.com/amanshenoy/multilogue-net

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Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation

https://github.com/anita-hu/MSAF

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MSAF: Multimodal Split Attention Fusion

https://github.com/butterfliesss/sdt

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A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations

https://github.com/csliuwei/MI_plot

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Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis

https://github.com/david-yoon/attentive-modality-hopping-for-SER

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Attentive Modality Hopping Mechanism for Speech Emotion Recognition

https://github.com/exploration-lab/cogmen

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COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

https://github.com/exploration-lab/shapes-of-emotion

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Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts

https://github.com/fufangze/SDR-GNN

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SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition

https://github.com/gw-zhong/cider

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Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts

https://github.com/haseebaslam95/MT-PKDOT

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Multi Teacher Privileged Knowledge Distillation for Multimodal Expression Recognition

https://github.com/ispamm/mhyeeg

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Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals

https://github.com/julieeF/CS231N-Project

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Emotion Recognition in Audio and Video Using Deep Neural Networks

https://github.com/katerynaCh/MMA-DFER

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MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild

https://github.com/lemei/affective-computing

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Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective

https://github.com/leson502/CORECT_EMNLP2023

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Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction

https://github.com/liangyubuaa/milmer

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Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition

https://github.com/liaolea/emoverse

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EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding

https://github.com/lijfrank-open/CFN-ESA

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CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition

https://github.com/lijfrank-open/GA2MIF

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GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection

https://github.com/lijfrank-open/GraphCFC

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GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition

https://github.com/lijfrank-open/GraphSmile

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Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition

https://github.com/mdswyz/dmd

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Decoupled Multimodal Distilling for Emotion Recognition

https://github.com/mintelligence-group/mmemorec

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VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition

https://github.com/mintelligence-group/speechimg_emorec

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Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data

https://github.com/mips-colt/mer-mce

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MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models

https://github.com/mmakiuchi/multimodal_emotion_recognition

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Multimodal Emotion Recognition with High-level Speech and Text Features

https://github.com/mygithub-ax/8th_abaw

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Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition

https://github.com/nicolas-richet/feature-vs-text-compound-emotion

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Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild

https://github.com/ppfliu/emotion-recognition

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Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition

https://github.com/praveena2j/cross-attentional-av-fusion

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Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.