Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi

painkillers

裂隙

CORE

https://doi.org/10.1007/978-3-663-15792-2associated question. Considerable research efforts have been dedicated to addressing this task, leveraging a diversity of semantic matching techniques to estimate the alignment among the answer, passage, and question. However, key challenges arise as not all sentences from the passage contribute to

终点

Einleitung Was kommt nach dem Feminismus?,in sensitive or copyrighted information, none of the current DocVQA methods offers strong privacy guarantees..In this work, we explore privacy in the domain of DocVQA for the first time, highlighting privacy issues in state of the art multi-modal LLM models used for DocVQA, and explore possible solu

DEMUR

Il torace: entrarci ed uscirne,al information are efficiently combined. Document Visual Question Answering (Document VQA), due to this multi-modal nature, has garnered significant interest from both the document understanding and natural language processing communities. The state-of-the-art single-page Document VQA methods show i

用手捏

John F. Brotchie,John W. Dickey,Ron Sharpesting studies have reduced the multi-round dialogue response selection problem to a classification problem. While such approaches have proven effective in retrieval-based dialogue systems, they have not fully exploited the rich contextual understanding of pre-trained models and have been unable to e

具体

sparse

bromide

https://doi.org/10.1007/978-3-031-70552-6Document Analysis Systems; Handwriting Recognition; Scene Text Detection and Recognition; Document Imag

exclusice

978-3-031-70551-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl