Zobrazeno 1 - 10
of 3 233
pro vyhledávání: '"Shah Ashish"'
Autor:
Ava C. Wilson, Joe Chiles, Shah Ashish, Diptiman Chanda, Preeti L. Kumar, James A. Mobley, Enid R. Neptune, Victor J. Thannickal, Merry-Lynn N. McDonald
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)
Abstract Fibrosis is a leading cause of morbidity and mortality worldwide. Although fibrosis may involve different organ systems, transforming growth factor-β (TGFβ) has been established as a master regulator of fibrosis across organs. Pirfenidone
Externí odkaz:
https://doaj.org/article/9701304c485e469590dc260ebd8077b2
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
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
Publikováno v:
Folia Medica, Vol 60, Iss 1, Pp 39-47 (2018)
The DNA repair process protects the cells from DNA damaging agent by multiple pathways. Majority of the cancer therapy cause DNA damage which leads to apoptosis. The cell has natural ability to repair this damage which ultimately leads to development
Externí odkaz:
https://doaj.org/article/86f6f571fdba416d93142a0553e20bd9
Autor:
Chiang, Ping-yeh, Zhou, Yipin, Poursaeed, Omid, Shukla, Satya Narayan, Shah, Ashish, Goldstein, Tom, Lim, Ser-Nam
Recently, Pyramid Adversarial training (Herrmann et al., 2022) has been shown to be very effective for improving clean accuracy and distribution-shift robustness of vision transformers. However, due to the iterative nature of adversarial training, th
Externí odkaz:
http://arxiv.org/abs/2312.16339
Autor:
Afham, Mohamed, Shukla, Satya Narayan, Poursaeed, Omid, Zhang, Pengchuan, Shah, Ashish, Lim, Sernam
While most modern video understanding models operate on short-range clips, real-world videos are often several minutes long with semantically consistent segments of variable length. A common approach to process long videos is applying a short-form vi
Externí odkaz:
http://arxiv.org/abs/2309.11569
Autor:
Mukhoti, Jishnu, Lin, Tsung-Yu, Poursaeed, Omid, Wang, Rui, Shah, Ashish, Torr, Philip H. S., Lim, Ser-Nam
We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of the vision encoder and the CLS token of the text encoder. With such an
Externí odkaz:
http://arxiv.org/abs/2212.04994
Autor:
Liu, Peirong, Wang, Rui, Zhang, Pengchuan, Poursaeed, Omid, Zhou, Yipin, Cao, Xuefei, Roy, Sreya Dutta, Shah, Ashish, Lim, Ser-Nam
Objection detection (OD) has been one of the most fundamental tasks in computer vision. Recent developments in deep learning have pushed the performance of image OD to new heights by learning-based, data-driven approaches. On the other hand, video OD
Externí odkaz:
http://arxiv.org/abs/2211.11077
Autor:
Mukhoti, Jishnu, Lin, Tsung-Yu, Chen, Bor-Chun, Shah, Ashish, Torr, Philip H. S., Dokania, Puneet K., Lim, Ser-Nam
In image classification, a lot of development has happened in detecting out-of-distribution (OoD) data. However, most OoD detection methods are evaluated on a standard set of datasets, arbitrarily different from training data. There is no clear defin
Externí odkaz:
http://arxiv.org/abs/2209.11960
Autor:
Scheinberg, Mila, Fortin, Travis, McCrosson, Matthew, Zhang, Ting Dan, Campos, Juan, Bernstein, Marc, Shah, Ashish
Publikováno v:
In The Journal of Foot and Ankle Surgery November-December 2024 63(6):747-751