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UC Berkeley and UCSF Researchers Propose Cross-Attention Masked Autoencoders (CrossMAE): A Leap in Efficient Visual Data Processing

One of the more intriguing developments in the dynamic field of computer vision is the efficient processing of visual data, which is essential for applications ranging from automated image analysis to the development of intelligent systems. A pressing challenge in this area is interpreting complex visual information, particularly in reconstructing detailed images from partial data.…

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Lingua Franca — Entity-Aware Machine Translation Approach for Question Answering over Knowledge Graphs | by Aleksandr Perevalov | Jan, 2024

Towards a lingua franca for knowledge graph question answering systems Machine Translation (MT) can enhance existing Question Answering (QA) systems, which have limited language capabilities, by enabling them to support multiple languages. However, there is one major drawback of MT — often, it fails at translating named entities that are not translatable word-by-word. For example,…

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