Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Shakeri, Fereshteh"'
Autor:
Shakeri, Fereshteh, Huang, Yunshi, Silva-Rodríguez, Julio, Bahig, Houda, Tang, An, Dolz, Jose, Ayed, Ismail Ben
Integrating image and text data through multi-modal learning has emerged as a new approach in medical imaging research, following its successful deployment in computer vision. While considerable efforts have been dedicated to establishing medical fou
Externí odkaz:
http://arxiv.org/abs/2409.03868
The development of vision-language models (VLMs) for histo-pathology has shown promising new usages and zero-shot performances. However, current approaches, which decompose large slides into smaller patches, focus solely on inductive classification,
Externí odkaz:
http://arxiv.org/abs/2409.01883
Autor:
Martin, Ségolène, Huang, Yunshi, Shakeri, Fereshteh, Pesquet, Jean-Christophe, Ayed, Ismail Ben
Transductive inference has been widely investigated in few-shot image classification, but completely overlooked in the recent, fast growing literature on adapting vision-langage models like CLIP. This paper addresses the transductive zero-shot and fe
Externí odkaz:
http://arxiv.org/abs/2405.18437
Autor:
Huang, Yunshi, Shakeri, Fereshteh, Dolz, Jose, Boudiaf, Malik, Bahig, Houda, Ayed, Ismail Ben
In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. In this w
Externí odkaz:
http://arxiv.org/abs/2404.02285
Autor:
Shakeri, Fereshteh, Boudiaf, Malik, Mohammadi, Sina, Sheth, Ivaxi, Havaei, Mohammad, Ayed, Ismail Ben, Kahou, Samira Ebrahimi
Few-shot learning has recently attracted wide interest in image classification, but almost all the current public benchmarks are focused on natural images. The few-shot paradigm is highly relevant in medical-imaging applications due to the scarcity o
Externí odkaz:
http://arxiv.org/abs/2206.00092