39 citations · 57 across the 10 of their papers we have counts for
10 papers
Classifier-Guided Captioning Across Modalities
Ariel Shaulov, Tal Shaharabany, Eitan Shaar +2
Most current captioning systems use language models trained on data from specific settings, such as image-based captioning via Amazon Mechanical Turk, limiting their ability to gen…
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…
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…
Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models
Tomer Amit, Shmuel Shichrur, Tal Shaharabany +1
A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, w…
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…
Explainability Guided Multi-Site COVID-19 CT Classification
Ameen Ali, Tal Shaharabany, Lior Wolf
Radiologist examination of chest CT is an effective way for screening COVID-19 cases. In this work, we overcome three challenges in the automation of this process: (i) the limited…
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…