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Titlebook: Analysis of Images, Social Networks and Texts; 11th International C Dmitry I. Ignatov,Michael Khachay,Sergey Zagoruyko Conference proceedin

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21#
發(fā)表于 2025-3-25 05:46:58 | 只看該作者
Threatening Expression and Target Identification in Under-Resource Languages Using NLP Techniquesrning technique with grid search fine-tuning. The proposed framework is compared with state-of-the art baseline and ten comparable models and it outperformed all for both tasks (threatening expression and target identification). Furthermore, the proposed framework obtained benchmark performance and
22#
發(fā)表于 2025-3-25 10:02:43 | 只看該作者
23#
發(fā)表于 2025-3-25 15:11:36 | 只看該作者
24#
發(fā)表于 2025-3-25 17:01:48 | 只看該作者
Less than Necessary or?More than Sufficient: Validating Probing Dataset Sizeite, finding that it can be safely reduced. Our experiments are conducted for two models, BERT and RoBERTa, showing the latter to consistently require more data. Fractions probing can be used to analogously investigate other datasets and models.
25#
發(fā)表于 2025-3-25 20:30:56 | 只看該作者
26#
發(fā)表于 2025-3-26 02:09:26 | 只看該作者
Automatic Detection of?Dialectal Features of?Pskov Dialects in?the?Speech of?Native Speakersapproaches to detecting dialect features, and identified the advantages and disadvantages of each. Ultimately, we developed a unified algorithm that incorporates the best of the approaches considered, it takes a .wav file as input and returns .TextGrid and .eaf files with annotations and detected di
27#
發(fā)表于 2025-3-26 06:25:24 | 只看該作者
MiVOLO: Multi-input Transformer for?Age and?Gender EstimationImages Dataset. The ground truth annotations for this benchmark have been meticulously generated by human annotators, resulting in high accuracy answers due to the smart aggregation of votes. Furthermore, we compare our model’s age recognition performance with human-level accuracy and demonstrate th
28#
發(fā)表于 2025-3-26 12:22:23 | 只看該作者
Handwritten Text Recognition and?Browsing in?Archive of?Prisoners’ Letters from?Smolensk Convict Priors. The search engine reached a precision of 97.14% and a recall of 91.35%. Visualization of the results provided highlighting of the found words on the original images. The study conducted demonstrates the possibility of creating a navigation system and its fitting to a specific handwriting with a
29#
發(fā)表于 2025-3-26 14:35:21 | 只看該作者
30#
發(fā)表于 2025-3-26 20:16:43 | 只看該作者
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