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

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Deep Learning-Driven Innovative Model for?Generating Functional Knowledge Unitsscribe the functional design knowledge. Nowadays, the acquisition of functional units is mainly manual, which is time-consuming and labor-intensive. Functional knowledge integration is an effective way to achieve innovation design, yet the insufficient functional units cannot effectively support the

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Global-SEG: Text Semantic Segmentation Based on?Global Semantic Pair Relationsc blocks. This paper introduces a new perspective on this task by utilizing global semantic pair relations from both token- and sentence-level language models. This approach addresses the limitations of prior work, which concentrated solely on individual semantic units like sentences. Our model proc

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Multimodal Adaptive Inference for?Document Image Classification with?Anytime Early Exiting understanding (VDU) tasks. Currently, there is a reliance on large document foundation models that offer advanced capabilities but come with a heavy computational burden. In this paper, we propose a multimodal early exit (EE) model design that incorporates various training strategies, exit layer ty

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0302-9743 ng document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more...?.978-3-031-70545-8978-3-031-70546-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

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hout any specific pre-training and with fewer parameters. We also report an extensive ablation study performed on FUNSD, highlighting the great impact of certain features and modelization choices on the performances.

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