472 citations · 647 across the 20 of their papers we have counts for
20 papers
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…
Fast 4D Mesh Generation by Spatio-Temporal Attention Chains
Dvir Samuel, Yuval Atzmon, Gal Chechik +1
4D mesh generation has recently emerged as a powerful paradigm for recovering dynamic 3D structure from videos, but existing methods remain slow, computationally expensive, and dif…
Data-Driven Loss Functions for Inference-Time Optimization in Text-to-Image
Sapir Esther Yiflach, Yuval Atzmon, Gal Chechik
Text-to-image diffusion models can generate stunning visuals, yet they often fail at tasks children find trivial--like placing a dog to the right of a teddy bear rather than to the…
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…
Lay-A-Scene: Personalized 3D Object Arrangement Using Text-to-Image Priors
Ohad Rahamim, Hilit Segev, Idan Achituve +3
Generating 3D visual scenes is at the forefront of visual generative AI, but current 3D generation techniques struggle with generating scenes with multiple high-resolution objects.…
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…
Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models
Moab Arar, Rinon Gal, Yuval Atzmon +4
Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts. Recently, encode…
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…
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
Rinon Gal, Moab Arar, Yuval Atzmon +3
Text-to-image personalization aims to teach a pre-trained diffusion model to reason about novel, user provided concepts, embedding them into new scenes guided by natural language p…
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon +4
Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific uni…
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
Niv Cohen, Rinon Gal, Eli A. Meirom +2
Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V&L problems. However, it is unclear how they can be used for r…
Learning to reason about and to act on physical cascading events
Yuval Atzmon, Eli A. Meirom, Shie Mannor +1
Reasoning and interacting with dynamic environments is a fundamental problem in AI, but it becomes extremely challenging when actions can trigger cascades of cross-dependent events…
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization
Tzuf Paz-Argaman, Yuval Atzmon, Gal Chechik +1
We study the problem of recognizing visual entities from the textual descriptions of their classes. Specifically, given birds' images with free-text descriptions of their species,…
A causal view of compositional zero-shot recognition
Yuval Atzmon, Felix Kreuk, Uri Shalit +1
People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domai…
From Generalized zero-shot learning to long-tail with class descriptors
Dvir Samuel, Yuval Atzmon, Gal Chechik
Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. Often, classes can be accompani…
Cooperative image captioning
Gilad Vered, Gal Oren, Yuval Atzmon +1
When describing images with natural language, the descriptions can be made more informative if tuned using downstream tasks. This is often achieved by training two networks: a "spe…
Few-Shot Learning with Per-Sample Rich Supervision
Roman Visotsky, Yuval Atzmon, Gal Chechik
Learning with few samples is a major challenge for parameter-rich models like deep networks. In contrast, people learn complex new concepts even from very few examples, suggesting…
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning
Yuval Atzmon, Gal Chechik
Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes…
Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning
Yuval Atzmon, Gal Chechik
In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given semantic information about the class, like a textua…
Learning to generalize to new compositions in image understanding
Yuval Atzmon, Jonathan Berant, Vahid Kezami +2
Recurrent neural networks have recently been used for learning to describe images using natural language. However, it has been observed that these models generalize poorly to scene…