3 accepted papers
Multimodal sentiment analysis recognizes emotions through text, audio, and visual modalities, but data incompleteness is a major challenge. Existing methods often focus on specific types of deficiencies and perform poorly when multiple types of noise are present simultaneously. To address this issue…
The methods based on multimodal representation learning enhance discriminable sentiment expression for multimodal sentiment analysis(MSA). The modal invariant and specific features serve different purposes in sentiment learning and the diversity of inter-sample and inter-category relationships takes…
Laplacian dimensionality reduction can effectively achieve feature transformation and preserve the important structure of high-dimensional features. However, the trained model with this method usually require better generalization ability to new samples. Hence, a human activity recognition method ba…