Publicacions CVC -- Edit Record

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	You must login to submit this form! Login Quick Search: Field: contains: ...
	Edit the following record:

Author	...				is Editor
Title	...			Type
Year	...	Publication	...	Abbreviated Journal	...
Volume	...	Issue	...	Pages	...
Keywords	...
Abstract	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: this https URL.
Address	...
Corporate Author	...			Thesis
Publisher	...	Place of Publication	...	Editor	...
Language	...	Summary Language	...	Original Title	...
Series Editor	...	Series Title	...	Abbreviated Series Title	...
Series Volume	...	Series Issue	...	Edition	...
ISSN	...	ISBN	...	Medium	...
Area	...	Expedition	...	Conference	...
Notes	...			Approved	yes no
Location
Call Number	...			Serial
Marked	yes no	Copy		Selected	yes no
User Keys	...
User Notes	...			User File	...
User Groups	...			Cite Key	...
Related	...
File
URL	...			DOI	...
	Online publication. Cite with this text: ...

Location Field:	my name & email address

Home

SQL Search | Library Search | Show Record | Extract Citations

Help