Emotion recognition has recently attracted extensive interest due to its significant
applications to human–computer interaction. The expression of human emotion depends on …

Compared to traditional sentiment analysis, which only considers text, multimodal sentiment
analysis needs to consider emotional signals from multimodal sources simultaneously and …

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for
Indonesian languages, including opening access to previously non-public resources …

Object hallucination poses a significant challenge in vision-language (VL) models, often
leading to the generation of nonsensical or unfaithful responses with non-existent objects …

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 …

Methods for detecting emotions that employ many modalities at the same time have been
found to be more accurate and resilient than those that rely on a single sense. This is due to …

Abstract Multimodal Emotion Recognition in Multiparty Conversations (MERMC) has
recently attracted considerable attention. Due to the complexity of visual scenes in multi …

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 …

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 …

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