Zobrazeno 1 - 10
of 18 052
pro vyhledávání: '"Ryoo By"'
Autor:
Park, Jongwoo, Ranasinghe, Kanchana, Kahatapitiya, Kumara, Ryoo, Wonjeong, Kim, Donghyun, Ryoo, Michael S.
Long-form videos that span across wide temporal intervals are highly information redundant and contain multiple distinct events or entities that are often loosely-related. Therefore, when performing long-form video question answering (LVQA),all infor
Externí odkaz:
http://arxiv.org/abs/2406.09396
Adapting deep learning networks for point cloud data recognition in self-driving vehicles faces challenges due to the variability in datasets and sensor technologies, emphasizing the need for adaptive techniques to maintain accuracy across different
Externí odkaz:
http://arxiv.org/abs/2407.04833
Autor:
Williams, Ezekiel, Ryoo, Avery Hee-Woon, Jiralerspong, Thomas, Payeur, Alexandre, Perich, Matthew G., Mazzucato, Luca, Lajoie, Guillaume
Landmark universal function approximation results for neural networks with trained weights and biases provided impetus for the ubiquitous use of neural networks as learning models in Artificial Intelligence (AI) and neuroscience. Recent work has push
Externí odkaz:
http://arxiv.org/abs/2407.00957
Autor:
Li, Xiang, Mata, Cristina, Park, Jongwoo, Kahatapitiya, Kumara, Jang, Yoo Sung, Shang, Jinghuan, Ranasinghe, Kanchana, Burgert, Ryan, Cai, Mu, Lee, Yong Jae, Ryoo, Michael S.
Large Language Models (LLMs) equipped with extensive world knowledge and strong reasoning skills can tackle diverse tasks across domains, often by posing them as conversation-style instruction-response pairs. In this paper, we propose LLaRA: Large La
Externí odkaz:
http://arxiv.org/abs/2406.20095
Extreme learning machine (ELM) is a methodology for solving partial differential equations (PDEs) using a single hidden layer feed-forward neural network. It presets the weight/bias coefficients in the hidden layer with random values, which remain fi
Externí odkaz:
http://arxiv.org/abs/2406.15959
Autor:
Ranasinghe, Kanchana, Shukla, Satya Narayan, Poursaeed, Omid, Ryoo, Michael S., Lin, Tsung-Yu
Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks, particularly for visual question answering (VQA). However, existing V-LLMs (e.g. BL
Externí odkaz:
http://arxiv.org/abs/2404.07449
Large Language Models (LLMs), known to contain a strong awareness of world knowledge, have allowed recent approaches to achieve excellent performance on Long-Video Understanding benchmarks, but at high inference costs. In this work, we first propose
Externí odkaz:
http://arxiv.org/abs/2403.16998
Language has become a prominent modality in computer vision with the rise of multi-modal LLMs. Despite supporting long context-lengths, their effectiveness in handling long-term information gradually declines with input length. This becomes critical,
Externí odkaz:
http://arxiv.org/abs/2403.14622
Autor:
Wang, Xin (Shane) (AUTHOR), Ryoo, Jun Hyun (Joseph) (AUTHOR), Campbell, Margaret C (AUTHOR) margaret.campbell@ucr.edu, Inman, J Jeffrey (AUTHOR)
Publikováno v:
Journal of Consumer Research. Jun2024, Vol. 51 Issue 1, p169-179. 11p.
Autor:
Ryoo, Kwangrok, Jo, Yeonsik, Lee, Seungjun, Kim, Mira, Jo, Ahra, Kim, Seung Hwan, Kim, Seungryong, Lee, Soonyoung
For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes. However, previous studies have only addres
Externí odkaz:
http://arxiv.org/abs/2312.13822