14 citations · 23 across the 4 of their papers we have counts for
4 papers
Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models
Yoad Tewel, Rinon Gal, Dvir Samuel +3
Adding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the original scene and seamlessly integ…
Make It Count: Text-to-Image Generation with an Accurate Number of Objects
Lital Binyamin, Yoad Tewel, Hilit Segev +3
Despite the unprecedented success of text-to-image diffusion models, controlling the number of depicted objects using text is surprisingly hard. This is important for various appli…
Zero-Shot Video Captioning with Evolving Pseudo-Tokens
Yoad Tewel, Yoav Shalev, Roy Nadler +2
We introduce a zero-shot video captioning method that employs two frozen networks: the GPT-2 language model and the CLIP image-text matching model. The matching score is used to st…
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…