Alternatives and similar repositories for AGHMN

Users that are interested in AGHMN are comparing it to the libraries listed below

• victorywys / RAVEN
  ☆66Updated 6 years ago
• mianzhang / dialogue_gcn
  Pytorch implementation to paper "DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation".
  ☆55Updated 4 years ago
• RaleLee / DialogueGCN
  A preprocessing and training code for DialogueGCN on Dailydialogue and Mastodon dataset. Use Bert base to preprocess the sentences. Based…
  ☆28Updated 3 years ago
• amanshenoy / multilogue-net
  Official PyTorch implementation of Multilogue-Net (Best paper runner-up at Challenge-HML @ ACL 2020)
  ☆58Updated 2 years ago
• declare-lab / contextual-utterance-level-multimodal-sentiment-analysis
  Context-Dependent Sentiment Analysis in User-Generated Videos
  ☆125Updated 2 years ago
• wxjiao / HiGRUs
  Implementation of the paper "Hierarchical GRU for Utterance-level Emotion Recognition" in NAACL-2019.
  ☆69Updated 4 years ago
• sverma88 / DeepCU-IJCAI19
  DeepCU: Integrating Both Common and Unique Latent Information for Multimodal Sentiment Analysis, IJCAI-19
  ☆19Updated 5 years ago
• soujanyaporia / contextual-multimodal-fusion
  Contextual inter modal attention for multimodal sentiment analysis
  ☆45Updated 4 years ago
• GussailRaat / EMNLP-19-IIM
  Context-aware-Interactive-Attention-for-Multi-modal-Sentiment-and Emotion-Analysis
  ☆11Updated 4 years ago
• shenwzh3 / DialogXL
  The PyTorch implementaion for AAAI-2021 accepted paper DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition.
  ☆48Updated 4 years ago
• zerohd4869 / DialogueCRN
  Source code for ACL-IJCNLP 2021 paper "DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations".
  ☆59Updated last year
• pliang279 / MFN
  [AAAI 2018] Memory Fusion Network for Multi-view Sequential Learning
  ☆114Updated 5 years ago
• somethingx678 / TodKat
  Transformer encoder-decoder for emotion detection in dialogues
  ☆62Updated 2 years ago
• shenwzh3 / DAG-ERC
  Pytorch code for ACL-IJCNLP accepted paper "Directed Acyclic Graph Network for Conversational Emotion Recognition"
  ☆84Updated 4 years ago
• xunan0812 / MIMN
  Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis - AAAI 2019
  ☆96Updated 5 years ago
• hainow / MCTN
  ☆48Updated 6 years ago
• martinmamql / multimodal_routing
  ☆19Updated 2 years ago
• Kaicheng-Yang0828 / Multimodal-Sentiment-Analysis-Paper-list
  This paper list is about multimodal sentiment analysis.
  ☆32Updated 3 years ago
• peymanbateni / multimodal-emotion-analysis-in-conversations
  Multi-model analysis of sentiment and emotion in multi-speaker conversations.
  ☆27Updated 2 years ago
• zhongpeixiang / KET
  The PyTorch code for paper "Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations"
  ☆80Updated 5 years ago
• jbdel / MOSEI_UMONS
  A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis
  ☆126Updated 6 months ago
• tae898 / multimodal-datasets
  Multimodal datasets.
  ☆30Updated last year
• thuiar / AWESOME-MSA
  Paper List for Multimodal Sentiment Analysis
  ☆101Updated 4 years ago
• chuyuanli / MTL4Depr
  Source code for paper Multi-Task Learning for Depression Detection in Dialogs (SIGDial 2022)
  ☆11Updated 8 months ago
• Justin1904 / TensorFusionNetworks
  Pytorch Implementation of Tensor Fusion Networks for multimodal sentiment analysis.
  ☆188Updated 5 years ago
• Exploration-Lab / Shapes-of-Emotion
  ☆14Updated 2 years ago
• ppfliu / emotion-recognition
  Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition
  ☆14Updated 3 years ago
• lstappen / MuSe2020
  Accompany code to reproduce the baselines of the International Multimodal Sentiment Analysis Challenge (MuSe 2020).
  ☆16Updated 2 years ago
• WasifurRahman / BERT_multimodal_transformer
  ☆210Updated 3 years ago
• gguibon / ProtoSeq
  Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks
  ☆13Updated 3 years ago