11 citations · 11 across the 8 of their papers we have counts for
5 papers · 1 filter
UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
Long Zhou, Fereshteh Shakeri, Aymen Sadraoui +3
Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the prediction statistic…
Few-shot Adaptation of Medical Vision-Language Models
Fereshteh Shakeri, Yunshi Huang, Julio Silva-Rodríguez +4
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. Whil…
Boosting Vision-Language Models for Histopathology Classification: Predict all at once
Maxime Zanella, Fereshteh Shakeri, Yunshi Huang +2
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 s…
Transductive Zero-Shot and Few-Shot CLIP
Ségolène Martin, Yunshi Huang, Fereshteh Shakeri +2
Transductive inference has been widely investigated in few-shot image classification, but completely overlooked in the recent, fast growing literature on adapting vision-langage mo…
LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP
Yunshi Huang, Fereshteh Shakeri, Jose Dolz +3
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…