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

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Einleitung Was kommt nach dem Feminismus?,ate that non-private models tend to memorise, a behaviour that can lead to exposing private information. We then evaluate baseline training schemes employing federated learning and differential privacy in this multi-modal scenario, where the sensitive information might be exposed through either or b

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Il torace: entrarci ed uscirne,r approach utilizes a self-attention scoring mechanism to generate relevance scores for each document page, enabling the retrieval of pertinent pages. This adaptation allows us to extend single-page Document VQA models to multi-page scenarios without constraints on the number of pages during evaluat

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John F. Brotchie,John W. Dickey,Ron Sharpeion method. In the prompt-tuning phase, a pairwise optimization fine-tuning strategy is employed to improve the model’s ability to effectively discriminate between positive and negative samples. In all three datasets, FPPP outperforms the baseline model, resulting in an improvement of the R10@1 metr

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,Complementi di topologia generale ?, comprehensiveness of our multimodal summaries. Our proposed methodology attains excellent results, surpassing other text summarization approaches tailored for the specified Indian languages. Furthermore, we enhance the significance of our work by incorporating a user satisfaction evaluation method,

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Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng

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UniVIE: A Unified Label Space Approach to?Visual Information Extraction from?Form-Like Documents proposals through a Tree Proposal Network, which are subsequently refined into hierarchical trees by a Relation Decoder module. To enhance the relation prediction capabilities of UniVIE, we incorporate two novel tree constraints into the Relation Decoder: a Tree Attention Mask and a Tree Level Embe

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Extractive Question Answering with?Contrastive Puzzles and?Reweighted Cluesthe weights of . samples, respectively. The experimental results, conducted on three benchmark datasets, demonstrates the superior performance of the proposed . compared to conventional approaches, highlighting its efficacy in mitigating the label noise and achieving enhanced EQA performance.

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CHIC: Corporate Document for?Visual Question Answeringmails). DocVQA, for its part, has several types of documents but only 4.5% of them are business documents (i.e. invoice, purchase order, etc.). All of these 4.5% of documents do not meet the diversity of documents that companies may encounter in their daily document flow. In order to extend these li

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