Zobrazeno 1 - 10
of 4 898
pro vyhledávání: '"Jang Young In"'
Autor:
Kim, Takyoung, Lee, Kyungjae, Jang, Young Rok, Cho, Ji Yong, Kim, Gangwoo, Cho, Minseok, Lee, Moontae
Interactions with billion-scale large language models typically yield long-form responses due to their extensive parametric capacities, along with retrieval-augmented features. While detailed responses provide insightful viewpoint of a specific subje
Externí odkaz:
http://arxiv.org/abs/2407.01158
While advancements in Vision Language Models (VLMs) have significantly improved the alignment of visual and textual data, these models primarily focus on aligning images with short descriptive captions. This focus limits their ability to handle compl
Externí odkaz:
http://arxiv.org/abs/2407.09541
``Learning to hash'' is a practical solution for efficient retrieval, offering fast search speed and low storage cost. It is widely applied in various applications, such as image-text cross-modal search. In this paper, we explore the potential of enh
Externí odkaz:
http://arxiv.org/abs/2405.14726
Autor:
Jang, Young Kyun, Lim, Ser-nam
Modern retrieval systems often struggle with upgrading to new and more powerful models due to the incompatibility of embeddings between the old and new models. This necessitates a costly process known as backfilling, which involves re-computing the e
Externí odkaz:
http://arxiv.org/abs/2405.14715
Composed Image Retrieval (CIR) is a complex task that retrieves images using a query, which is configured with an image and a caption that describes desired modifications to that image. Supervised CIR approaches have shown strong performance, but the
Externí odkaz:
http://arxiv.org/abs/2405.00571
Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification. Current techniques rely on supervised learning for CIR models using labeled triplets of the reference image, text, target ima
Externí odkaz:
http://arxiv.org/abs/2404.15516
Autor:
He, Bo, Li, Hengduo, Jang, Young Kyun, Jia, Menglin, Cao, Xuefei, Shah, Ashish, Shrivastava, Abhinav, Lim, Ser-Nam
With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However, existing LLM-based large multimodal models (e.g., Video-LLaMA, VideoC
Externí odkaz:
http://arxiv.org/abs/2404.05726
Recent advances in instruction tuning have led to the development of State-of-the-Art Large Multimodal Models (LMMs). Given the novelty of these models, the impact of visual adversarial attacks on LMMs has not been thoroughly examined. We conduct a c
Externí odkaz:
http://arxiv.org/abs/2312.03777
Autor:
Lee, Eun Sung, Cha, Byung Seok, Jang, Young Jun, Woo, Jisu, Kim, Seokjoon, Park, Sung-Soo, Oh, Seung Wook, Park, Ki Soo
Publikováno v:
In International Journal of Biological Macromolecules November 2024 280 Part 3
Autor:
Jeon, Ho Sung, Kim, Young In, Lee, Jung-Hee, Park, Young Jun, Son, Jung-Woo, Lee, Jun-Won, Youn, Young Jin, Ahn, Min-Soo, Kim, Jang-Young, Yoo, Byung-Su, Ko, Sung Min, Ahn, Sung Gyun
Publikováno v:
In JACC: Cardiovascular Interventions 14 October 2024 17(19):2216-2225