39 citations · 57 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Learning a Weight Map for Weakly-Supervised Localization
Tal Shaharabany, Lior Wolf
In the weakly supervised localization setting, supervision is given as an image-level label. We propose to employ an image classifier and to train a generative network that…
End to End Trainable Active Contours via Differentiable Rendering
Shir Gur, Tal Shaharabany, Lior Wolf
We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map…