Multi-modal learning with GNNs

Multi-modal learning involves processing and relating information from multiple types of data sources or sensory inputs. In the context of CV, this often means combining visual data with other modalities such as text, audio, or sensor data. GNNs provide a powerful framework for multi-modal learning by naturally representing different types of data and their inter-relationships in a unified graph structure. This section will explore how GNNs can be applied to multi-modal learning tasks in CV.

Integrating visual and textual information using graphs

One of the most common multi-modal pairings in CV is the combination of visual and textual data. This integration is crucial for tasks such as image captioning, visual question answering, and text-based image retrieval. GNNs offer a natural way to represent and process these two modalities in a single framework.

For example, consider a visual question-answering task. We can construct a graph where nodes...