Competition

Privacy Preserving Federated Learning Document VQA

Dimosthenis Karatzas · Rubèn Tito · Lei Kang · Mohamed Ali Souibgui · Khanh Nguyen · Raouf Kerkouche · Kangsoo Jung · Marlon Tobaben · Joonas Jälkö · Vincent Poulain d'Andecy · Aurélie JOSEPH · Ernest Valveny · Josep Llados · Antti Honkela · Mario Fritz

Room 356
[ ] [ Project Page ]
Fri 15 Dec 7 a.m. PST — 10 a.m. PST

Abstract:

In an era of increasing digitalization and data-driven decision-making, the intersection of document intelligence and privacy has become a critical concern. The Privacy-Preserving Federated Learning Document Visual Question Answering Workshop aims to bring together experts, researchers, and practitioners to explore innovative solutions and discuss the latest advancements in this crucial field.

Join us for insightful invited talks by leading figures in the field. These talks will provide valuable perspectives on the current state of privacy-preserving document intelligence and its future directions. Get an in-depth look at the Privacy-Preserving Document Visual Question Answering Competition that we are currently holding, with a detailed overview of the competition, the dataset, and the competition results. Moreover, the top winners of the competition will have the opportunity to give short talks about their winning methods and strategies. Gain firsthand insights into the innovative approaches that led to their success.

Workshop URL: https://sites.google.com/view/pfldocvqa-neurips-23/home
Associated Competition URL: https://benchmarks.elsa-ai.eu/?ch=2

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