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

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僵硬

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/284813.jpg

MOCK

https://doi.org/10.1007/978-3-662-13217-3r a source-free DA approach to adapt a given trained model to a new collection—an extremely useful scenario for preserving musical heritage. The method involves re-training the pre-trained model to align the statistics stored from the original data in normalization layers with those of the new colle

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陰謀小團(tuán)體

Gerd Lütjering,James C. Williamse only recently been considered in this process. This results in a lack of adequate ground truth datasets needed for the development and benchmarking of OMR systems. In this work, the KuiSCIMA (Jiang Kui Score Images for Musicological Analysis) dataset is introduced. KuiSCIMA is the first machine-re

木質(zhì)

木質(zhì)

disparage

Bundeszentrale für politische Bildunge of Pretrained Language Models (PLMs) has sparked increased interest in leveraging these models for EQA tasks, yielding promising results. Nonetheless, current approaches frequently neglect the issue of label noise, which arises from incomplete labeling and inconsistent annotations, thereby reducin

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Tokolyse beim vorzeitigen Blasensprung?,k of selecting the correct text to use in a comic panel, given its neighbouring panels. Traditional methods based on recurrent neural networks, have struggled with this task due to limited OCR accuracy and inherent model limitations. We introduce a novel Multimodal Large Language Model (Multimodal-L