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/praveena2j/jointcrossattentional-av-fusion

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A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition

https://github.com/praveena2j/rjcma

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Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion Recognition

https://github.com/qlwei89/fv2es

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FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference

https://github.com/ricfrr/mpuc-mer

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Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss

https://github.com/shamanez/BERT-like-is-All-You-Need

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Jointly Fine-Tuning "BERT-like" Self Supervised Models to Improve Multimodal Speech Emotion Recognition

https://github.com/shravan-18/AVTCA

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Multimodal Emotion Recognition using Audio-Video Transformer Fusion with Cross Attention

https://github.com/shuzihuaiyu/tacfn

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TACFN: Transformer-based Adaptive Cross-modal Fusion Network for Multimodal Emotion Recognition

https://github.com/skeletonnn/cfn-sr

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A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition

https://github.com/smwanghhh/co-sa

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Cooperative Sentiment Agents for Multimodal Sentiment Analysis

https://github.com/sreyan88/mmer

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MMER: Multimodal Multi-task Learning for Speech Emotion Recognition

https://github.com/tmacmai/multimodal-information-bottleneck

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Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations

https://github.com/vanntc711/multidag-cl

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Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition

https://github.com/wenliangdai/Modality-Transferable-MER

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Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition

https://github.com/wooyoohl/mer2024-semi

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Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples

https://github.com/wooyoohl/noise-robust_mer

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Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios

https://github.com/wykstc/MERC-main

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Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition

https://github.com/xiaoyaoxinyi/m2se

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

https://github.com/xuecwu/emotions

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Towards Emotion Analysis in Short-form Videos: A Large-Scale Dataset and Baseline

https://github.com/zebangcheng/emotion-llama

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Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning

https://github.com/zeroQiaoba/EmotiW2018

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Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition

https://github.com/zeroqiaoba/gpt4v-emotion

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GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition

https://github.com/zeroqiaoba/mer2023-baseline

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MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

https://github.com/zeroqiaoba/mertools

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MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition

https://github.com/zhuoyulang/if-mmin

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Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities

https://github.com/zxpoqas123/MCT-HFR

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Modality-Collaborative Transformer with Hybrid Feature Reconstruction for Robust Emotion Recognition

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