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pro vyhledávání: '"Ng, Youyang"'
In this paper, we rethink sparse lexical representations for image retrieval. By utilizing multi-modal large language models (M-LLMs) that support visual prompting, we can extract image features and convert them into textual data, enabling us to util
Externí odkaz:
http://arxiv.org/abs/2408.16296
Autor:
Nara, Ryoya, Lin, Yu-Chieh, Nozawa, Yuji, Ng, Youyang, Itoh, Goh, Torii, Osamu, Matsui, Yusuke
Many image retrieval studies use metric learning to train an image encoder. However, metric learning cannot handle differences in users' preferences, and requires data to train an image encoder. To overcome these limitations, we revisit relevance fee
Externí odkaz:
http://arxiv.org/abs/2404.16398
Autor:
Hoshi, Yasuto, Miyashita, Daisuke, Ng, Youyang, Tatsuno, Kento, Morioka, Yasuhiro, Torii, Osamu, Deguchi, Jun
Retrieval-augmented large language models (R-LLMs) combine pre-trained large language models (LLMs) with information retrieval systems to improve the accuracy of factual question-answering. However, current libraries for building R-LLMs provide high-
Externí odkaz:
http://arxiv.org/abs/2308.10633
Autor:
Ng, Youyang, Miyashita, Daisuke, Hoshi, Yasuto, Morioka, Yasuhiro, Torii, Osamu, Kodama, Tomoya, Deguchi, Jun
Large Language Model (LLM) based Generative AI systems have seen significant progress in recent years. Integrating a knowledge retrieval architecture allows for seamless integration of private data into publicly available Generative AI systems using
Externí odkaz:
http://arxiv.org/abs/2308.03983
Autor:
Hoshi, Yasuto, Miyashita, Daisuke, Morioka, Yasuhiro, Ng, Youyang, Torii, Osamu, Deguchi, Jun
Neural document retrievers, including dense passage retrieval (DPR), have outperformed classical lexical-matching retrievers, such as BM25, when fine-tuned and tested on specific question-answering datasets. However, it has been shown that the existi
Externí odkaz:
http://arxiv.org/abs/2303.05153
In existing image classification systems that use deep neural networks, the knowledge needed for image classification is implicitly stored in model parameters. If users want to update this knowledge, then they need to fine-tune the model parameters.
Externí odkaz:
http://arxiv.org/abs/2204.01186