Multimodal Deep Learning
31 papers with code • 1 benchmarks • 3 datasets
Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data.
We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities.
Emotion Recognition is a challenging research area given its complex nature, and humans express emotional cues across various modalities such as language, facial expressions, and speech.
In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications.
We also identify dominating modality problem when training a multimodal descriptor.