In this paper, we address the challenge of visual geo-localization from low-quality UAV imagery captured in real world environments. We propose a two-stage architecture, which includes Super-Resolution and visual geo-localization. We introduced novel, non-learnable Ensemble Super-Resolution (ESR) module, which first refines upscaled aerial frames, then seamlessly feeds the enhanced imagery into a visual geo-localization pipeline.
This article presents a comprehensive approach to sentiment analysis in social networks by leveraging modern text processing methods and machine learning algorithms. The primary focus is the integration of the Sentence-BERT model for text vectorization and XGBoost for sentiment classification. Using the Sentiment140 dataset, an extensive study of text messages labeled with sentiment annotations was conducted. The Sentence-BERT model enables the generation of high-quality vector representations of textual data, preserving both lexical and contextual relationships between words.
A Question Generation System (QGS) is a sophisticated piece of AI technology designed to automatically generate questions from a given text, document, or context. Recently, this technology has gained significant attention in various fields, including education, and content creation. As AI continues to evolve, these systems are likely to become even more advanced and viewed as an inherent part of any modern e-learning or knowledge assessment system. In this research paper, we showcase the effectiveness of leveraging pre-trained checkpoints for Arabic questions generat
A method for data set formation has been developed to verify the ability of pre-trained models to learn transitivity dependencies. The generated data set was used to test the quality of learning the transitivity dependencies in the task of natural language inference (NLI). Testing of a data set with a size of 10,000 samples (MultiNLI) used to test the RoBerta model.