PgM segments multimodal representations into uni-modal and paired-modal features with cumulative-softmax gates and trains them with separate learners, reconstruction, and classification losses, yielding accuracy gains over the simple baselines tested.
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
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abstract
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.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Partitioner Guided Modal Learning Framework
PgM segments multimodal representations into uni-modal and paired-modal features with cumulative-softmax gates and trains them with separate learners, reconstruction, and classification losses, yielding accuracy gains over the simple baselines tested.