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pro vyhledávání: '"KNOWLEDGE GENERATION"'
Knowledge graph construction (KGC) from (semi-)structured data is challenging, and facilitating user involvement is an issue frequently brought up within this community. We cannot deny the progress we have made with respect to (declarative) knowledge
Externí odkaz:
http://arxiv.org/abs/2412.16766
Prompt learning has become a prevalent strategy for adapting vision-language foundation models (VLMs) such as CLIP to downstream tasks. With the emergence of large language models (LLMs), recent studies have explored the potential of using category-r
Externí odkaz:
http://arxiv.org/abs/2408.14812
Autor:
Konlan, Kennedy Diema1 (AUTHOR), Adedia, David2 (AUTHOR), Nyande, Felix K.3 (AUTHOR), Amenuke, Margaret4 (AUTHOR), Tackie, Vivian1 (AUTHOR), Anaman-Torgbor, Judith A.1 (AUTHOR) janaman@uhas.edu.gh
Publikováno v:
PLoS ONE. 12/12/2024, Vol. 19 Issue 12, p1-14. 14p.
Synthetic data generation has gained significant attention recently for its utility in training large vision and language models. However, the application of synthetic data to the training of multimodal context-augmented generation systems has been r
Externí odkaz:
http://arxiv.org/abs/2406.19593
Akademický článek
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Autor:
Cao, Rui, Jiang, Jing
Previous solutions to knowledge-based visual question answering~(K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model. Recently pre-trained LLMs have been used as both a knowledge source and a z
Externí odkaz:
http://arxiv.org/abs/2402.02541