Traditional CV approaches versus graph-based approaches

Traditional CV approaches primarily rely on CNNs that operate on regular grid structures, extracting features through convolution and pooling operations. While effective for many tasks, these methods often struggle with long-range dependencies and relational reasoning. In contrast, graph-based approaches represent visual data as nodes and edges, utilizing GNNs to process information. This structure allows for easier incorporation of non-local information and relational inductive biases.

For instance, in image classification, a CNN might have difficulty relating distant parts of an image, whereas a graph-based approach could represent different image regions as nodes and their relationships as edges, facilitating long-range reasoning. This fundamental difference in data representation and processing enables graph-based methods to overcome some of the limitations inherent in traditional CNN-based approaches, potentially leading...