activity
20212023
most citedAutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

39 citations · 48 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SD2023

Zero-Shot Audio Captioning via Audibility Guidance

Tal Shaharabany, Ariel Shaulov, Lior Wolf

The task of audio captioning is similar in essence to tasks such as image and video captioning. However, it has received much less attention. We propose three desiderata for captio…

cs.CV2023

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…

cs.CV202339 cited

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…

cs.CV20229 cited

What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs

Tal Shaharabany, Yoad Tewel, Lior Wolf

Given an input image, and nothing else, our method returns the bounding boxes of objects in the image and phrases that describe the objects. This is achieved within an open world p…

cs.CV2021

End-to-End Segmentation via Patch-wise Polygons Prediction

Tal Shaharabany, Lior Wolf

The leading segmentation methods represent the output map as a pixel grid. We study an alternative representation in which the object edges are modeled, per image patch, as a polyg…