Multimodal Machine Learning Applications

This cluster of papers focuses on the development and improvement of visual question answering systems, image captioning techniques, and neural networks for understanding and generating descriptions of images and videos. The research involves semantic reasoning, multimodal fusion, scene graph generation, attention mechanisms, and deep learning approaches to bridge the gap between vision and language.

How has Multimodal Machine Learning Applications's publication output changed over time?

Output grew0% over the shown period — from 2 works in 2015 to 2 in 2022.

What are the most-cited papers on Multimodal Machine Learning Applications?

Where is Multimodal Machine Learning Applications research published, and who funds it?

How much of the research on Multimodal Machine Learning Applications is open access?