14 citations · 41 across the 23 of their papers we have counts for
5 papers · 1 filter
CLIPAG: Towards Generator-Free Text-to-Image Generation
Roy Ganz, Michael Elad
Perceptually Aligned Gradients (PAG) refer to an intriguing property observed in robust image classification models, wherein their input gradients align with human perception and p…
FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions
Noam Rotstein, David Bensaid, Shaked Brody +2
The advent of vision-language pre-training techniques enhanced substantial progress in the development of models for image captioning. However, these models frequently produce gene…
Class-Conditioned Transformation for Enhanced Robust Image Classification
Tsachi Blau, Roy Ganz, Chaim Baskin +2
Robust classification methods predominantly concentrate on algorithms that address a specific threat model, resulting in ineffective defenses against other threat models. Real-worl…
CLIPTER: Looking at the Bigger Picture in Scene Text Recognition
Aviad Aberdam, David Bensaïd, Alona Golts +5
Reading text in real-world scenarios often requires understanding the context surrounding it, especially when dealing with poor-quality text. However, current scene text recognizer…
Towards Models that Can See and Read
Roy Ganz, Oren Nuriel, Aviad Aberdam +3
Visual Question Answering (VQA) and Image Captioning (CAP), which are among the most popular vision-language tasks, have analogous scene-text versions that require reasoning from t…