429 citations · 1.3k across the 86 of their papers we have counts for
7 papers · 2 filters
Multi-Dimensional Hyena for Spatial Inductive Bias
Itamar Zimerman, Lior Wolf
In recent years, Vision Transformers have attracted increasing interest from computer vision researchers. However, the advantage of these transformers over CNNs is only fully manif…
Box-based Refinement for Weakly Supervised and Unsupervised Localization Tasks
Eyal Gomel, Tal Shaharabany, Lior Wolf
It has been established that training a box-based detector network can enhance the localization performance of weakly supervised and unsupervised methods. Moreover, we extend this…
AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
Tal Shaharabany, Aviad Dahan, Raja Giryes +1
The recently introduced Segment Anything Model (SAM) combines a clever architecture and large quantities of training data to obtain remarkable image segmentation capabilities. Howe…
The Hidden Language of Diffusion Models
Hila Chefer, Oran Lang, Mor Geva +5
Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual prompt. However, the internal representations learn…
Discriminative Class Tokens for Text-to-Image Diffusion Models
Idan Schwartz, Vésteinn Snæbjarnarson, Hila Chefer +4
Recent advances in text-to-image diffusion models have enabled the generation of diverse and high-quality images. While impressive, the images often fall short of depicting subtle…
Gradient Adjusting Networks for Domain Inversion
Erez Sheffi, Michael Rotman, Lior Wolf
StyleGAN2 was demonstrated to be a powerful image generation engine that supports semantic editing. However, in order to manipulate a real-world image, one first needs to be able t…
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Hila Chefer, Yuval Alaluf, Yael Vinker +2
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, curre…