Zobrazeno 1 - 10
of 3 138
pro vyhledávání: '"Shah, Ashish"'
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
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
Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performance in the age of deep learning. Most of these state-of-the-art, howeve
Externí odkaz:
http://arxiv.org/abs/2112.00969
Autor:
Dokollari, Aleksander, Rosati, Fabrizio, Muneretto, Claudio, Amabile, Andrea, Pernoci, Marjela, Gemelli, Marco, Hassanabad, Ali Fatehi, Sicouri, Serge, Sicouri, Noah, Yamashita, Yoshiyuki, Baudo, Massimo, Bonacchi, Massimo, Cabrucci, Francesco, Bacchi, Beatrice, Ghorpade, Nitin, Shah, Ashish, Coku, Lindita, Cameli, Matteo, Mandoli, Giulia Elena, Kjelstrom, Stephanie, Montone, Georgia, Wertan, Maryann, Ramlawi, Basel, DiMagli, Arnaldo, Sutter, Francis P.
Publikováno v:
In The American Journal of Cardiology 15 August 2024 225:10-21
Publikováno v:
In Current Problems in Cardiology August 2024 49(8)