  Object detection in documents is a key step to automate the structural
elements identification process in a digital or scanned document through
understanding the hierarchical structure and relationships between different
elements. Large and complex models, while achieving high accuracy, can be
computationally expensive and memory-intensive, making them impractical for
deployment on resource constrained devices. Knowledge distillation allows us to
create small and more efficient models that retain much of the performance of
their larger counterparts. Here we present a graph-based knowledge distillation
framework to correctly identify and localize the document objects in a document
image. Here, we design a structured graph with nodes containing proposal-level
features and edges representing the relationship between the different proposal
regions. Also, to reduce text bias an adaptive node sampling strategy is
designed to prune the weight distribution and put more weightage on non-text
nodes. We encode the complete graph as a knowledge representation and transfer
it from the teacher to the student through the proposed distillation loss by
effectively capturing both local and global information concurrently. Extensive
experimentation on competitive benchmarks demonstrates that the proposed
framework outperforms the current state-of-the-art approaches. The code will be
available at: https://github.com/ayanban011/GraphKD.
