14 citations · 36 across the 11 of their papers we have counts for
10 papers · 1 filter
Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching
Yoad Tewel, Yuval Atzmon, Gal Chechik +1
Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of paired examples. This limits sca…
Motion by Queries: Identity-Motion Trade-offs in Text-to-Video Generation
Yuval Atzmon, Rinon Gal, Yoad Tewel +2
Text-to-video diffusion models have shown remarkable progress in generating coherent video clips from textual descriptions. However, the interplay between motion, structure, and id…
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…
Training-Free Consistent Text-to-Image Generation
Yoad Tewel, Omri Kaduri, Rinon Gal +4
Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to cons…
Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models
Dvir Samuel, Barak Meiri, Haggai Maron +5
Diffusion inversion is the problem of taking an image and a text prompt that describes it and finding a noise latent that would generate the exact same image. Most current determin…
Key-Locked Rank One Editing for Text-to-Image Personalization
Yoad Tewel, Rinon Gal, Gal Chechik +1
Text-to-image models (T2I) offer a new level of flexibility by allowing users to guide the creative process through natural language. However, personalizing these models to align w…
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…
ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic
Yoad Tewel, Yoav Shalev, Idan Schwartz +1
Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. While such models can provide a powerful score for matc…