289 citations · 776 across the 10 of their papers we have counts for
12 papers
FETA: Towards Specializing Foundation Models for Expert Task Applications
Amit Alfassy, Assaf Arbelle, Oshri Halimi +10
Foundation Models (FMs) have demonstrated unprecedented capabilities including zero-shot learning, high fidelity data synthesis, and out of domain generalization. However, as we sh…
Detector-Free Weakly Supervised Grounding by Separation
Assaf Arbelle, Sivan Doveh, Amit Alfassy +14
Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with th…
ISP Distillation
Eli Schwartz, Alex Bronstein, Raja Giryes
Nowadays, many of the images captured are `observed' by machines only and not by humans, e.g., in autonomous systems. High-level machine vision models, such as object recognition o…
Fine-grained Angular Contrastive Learning with Coarse Labels
Guy Bukchin, Eli Schwartz, Kate Saenko +4
Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This…
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
Sivan Doveh, Eli Schwartz, Chao Xue +4
Few-Shot Learning (FSL) is a topic of rapidly growing interest. Typically, in FSL a model is trained on a dataset consisting of many small tasks (meta-tasks) and learns to adapt to…
Baby steps towards few-shot learning with multiple semantics
Eli Schwartz, Leonid Karlinsky, Rogerio Feris +2
Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems. While considerabl…