Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

Combining machine learning in neural networks with multimodal fusion strategies offers an
interesting potential for classification tasks but the optimum fusion strategies for many …

Accurate forecasting of social power demand is the country's primary task in making
decisions on power overall planning, coal power withdrawal, and renewable energy …

Recent developments in image classification and natural language processing, coupled with
the rapid growth in social media usage, have enabled fundamental advances in detecting …

Multimodal sentiment analysis is the challenging research area that attends to the fusion of
multiple heterogeneous modalities. The main challenge is the occurrence of some missing …

Research on event-based processing and analysis of media is receiving an increasing
attention from the scientific community due to its relevance for an abundance of applications …

Entity alignment is critical for multiple knowledge graphs (KGs) integration. Although
researchers have made significant efforts to explore the relational embeddings between …

The major challenge in audio-visual event localization task lies in how to fuse information
from multiple modalities effectively. Recent works have shown that the attention mechanism …

Multimodal information (eg, visual, acoustic, and textual) has been widely used to enhance
representation learning for micro-video recommendation. For integrating multimodal …

This paper strives for video event detection using a representation learned from deep
convolutional neural networks. Different from the leading approaches, who all learn from the …