Multimodal learning provides a path to fully utilize all types of information related to the
modeling target to provide the model with a global vision. Zero‐shot learning (ZSL) is a …

NLP research is impeded by a lack of resources and awareness of the challenges presented
by underrepresented languages and dialects. Focusing on the languages spoken in …

Learning effective joint embedding for cross-modal data has always been a focus in the field
of multimodal machine learning. We argue that during multimodal fusion, the generated …

State-of-the-art abstractive summarization models generally rely on extensive labeled data,
which lowers their generalization ability on domains where such data are not available. In …

Multimodal abstractive summarization (MAS) models that summarize videos (vision
modality) and their corresponding transcripts (text modality) are able to extract the essential …

Sarcasm is a case of implicit emotion and needs additional information like context and
multimodality for better detection. But sometimes, this additional information also fails to help …

Existing works on multimodal affective computing tasks, such as emotion recognition,
generally adopt a two-phase pipeline, first extracting feature representations for each single …

Emotion recognition has been an active research area for a long time. Recently, multimodal
emotion recognition from video data has grown in importance with the explosion of video …

Human brain extracts the spatial and temporal semantic information by processing the multi-
modalities, which has contextually meaningful for perceiving and understanding the …

Multimodal Emotion Recognition is challenging because of the heterogeneity gap among
different modalities. Due to the powerful ability of feature abstraction, Deep Neural Networks …