mlsanityData to 7 Oct 2026

M-SENA: An Integrated Platform for Multimodal Sentiment Analysis

arXiv 2203.12441 ↗First posted 23 Mar 2022cs.AI cs.MMcited by 21

Not on the map. Why?

Abstract (arXiv)

M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules. In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules. Reliable baseline results of different modality features and MSA benchmarks are also reported. Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results. The source code of the platform is publicly available at https://github.com/thuiar/M-SENA.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 22 of its 59 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

22 of 59 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  4. 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 637
  5. 2019MediaPipe: A Framework for Building Perception Pipelinescited by 572
  6. 2020Pre-trained Models for Natural Language Processing: A Surveycited by 504
  7. 2017Tensor Fusion Network for Multimodal Sentiment Analysiscited by 429
  8. 2018Efficient Low-rank Multimodal Fusion with Modality-Specific Factorscited by 268
  9. 2020MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysiscited by 262
  10. 2021Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysiscited by 179
  11. 2018Learning Factorized Multimodal Representationscited by 171
  12. 2018Memory Fusion Network for Multi-view Sequential Learningcited by 167
10 more
  1. 2021Emotion Recognition from Speech Using Wav2vec 2.0 Embeddingscited by 158
  2. 2021Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysiscited by 130
  3. 2018Multi-attention Recurrent Network for Human Communication Comprehensioncited by 89
  4. 2015Rendering of Eyes for Eye-Shape Registration and Gaze Estimationcited by 49
  5. 2021Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognitioncited by 47
  6. 2021Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysiscited by 45
  7. 2021Multimodal End-to-End Sparse Model for Emotion Recognitioncited by 40
  8. 2019Learning Representations from Imperfect Time Series Data via Tensor Rank Regularizationcited by 30
  9. 2017Benchmarking Multimodal Sentiment Analysiscited by 11
  10. 2019A Multimodal Emotion Sensing Platform for Building Emotion-Aware Applicationscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 3; 2025: 6; 2026: 4.

21 papers.

Sort:

By area:

  1. 2022UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognitioncited by 61
  2. 2022Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Modulecited by 43
  3. 2023Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysiscited by 27
  4. 2022Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysiscited by 20
  5. 2023Noise-Resistant Multimodal Transformer for Emotion Recognitioncited by 6
  6. 2024End-to-end Semantic-centric Video-based Multimodal Affective Computingcited by 4
  7. 2025A Principled Framework for Multi-View Contrastive Learningcited by 4
  8. 2023SeqAug: Sequential Feature Resampling as a modality agnostic augmentation methodcited by 3
  9. 2025DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysiscited by 3
  10. 2022Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysiscited by 2
  11. 2024Impact of Stickers on Multimodal Sentiment and Intent in Social Media: A New Task, Dataset and Baselinecited by 2
  12. 2024Evaluation of data inconsistency for multi-modal sentiment analysiscited by 2
9 more
  1. 2025MVP: Multimodal Emotion Recognition based on Video and Physiological Signalscited by 2
  2. 2025Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysiscited by 2
  3. 2023PowMix: A Versatile Regularizer for Multimodal Sentiment Analysiscited by 1
  4. 2025DashFusion: Dual-stream Alignment with Hierarchical Bottleneck Fusion for Multimodal Sentiment Analysiscited by 1
  5. 2026Progressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalitiescited by 1
  6. 2025Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecturecited by 0
  7. 2026GRAMformer: Any-Order Modality Interactions via Volumetric Multimodal Cross-Attentioncited by 0
  8. 2026Segregate, Refine, Integrate: Decomposing Multimodal Fusion for Sentiment Analysiscited by 0
  9. 2026MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysiscited by 0