Search Results for author: Yuval Atzmon

Found 14 papers, 6 papers with code

Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models

no code implementations13 Jul 2023 Moab Arar, Rinon Gal, Yuval Atzmon, Gal Chechik, Daniel Cohen-Or, Ariel Shamir, Amit H. Bermano

Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts.

Image Generation

Key-Locked Rank One Editing for Text-to-Image Personalization

no code implementations2 May 2023 Yoad Tewel, Rinon Gal, Gal Chechik, Yuval Atzmon

The task of T2I personalization poses multiple hard challenges, such as maintaining high visual fidelity while allowing creative control, combining multiple personalized concepts in a single image, and keeping a small model size.

Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models

no code implementations23 Feb 2023 Rinon Gal, Moab Arar, Yuval Atzmon, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or

Specifically, we employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain, e. g. a specific face, and learns to map it into a word-embedding representing the concept.

Novel Concepts

An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

6 code implementations2 Aug 2022 Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or

Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes.

"This is my unicorn, Fluffy": Personalizing frozen vision-language representations

2 code implementations4 Apr 2022 Niv Cohen, Rinon Gal, Eli A. Meirom, Gal Chechik, Yuval Atzmon

We propose an architecture for solving PerVL that operates by extending the input vocabulary of a pretrained model with new word embeddings for the new personalized concepts.

Image Retrieval Retrieval +3

Learning to reason about and to act on physical cascading events

no code implementations2 Feb 2022 Yuval Atzmon, Eli A. Meirom, Shie Mannor, Gal Chechik

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

1 code implementation Findings of the Association for Computational Linguistics 2020 Tzuf Paz-Argaman, Yuval Atzmon, Gal Chechik, Reut Tsarfaty

Specifically, given birds' images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions.

Zero-Shot Learning

A causal view of compositional zero-shot recognition

1 code implementation NeurIPS 2020 Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik

This leads to consistent misclassification of samples from a new distribution, like new combinations of known components.

Compositional Zero-Shot Learning

From Generalized zero-shot learning to long-tail with class descriptors

1 code implementation5 Apr 2020 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.

Few-Shot Learning Generalized Zero-Shot Learning +1

Cooperative image captioning

no code implementations26 Jul 2019 Gilad Vered, Gal Oren, Yuval Atzmon, Gal Chechik

Second, we show that the generated descriptions can be kept close to natural by constraining them to be similar to human descriptions.

Image Captioning

Few-Shot Learning with Per-Sample Rich Supervision

no code implementations10 Jun 2019 Roman Visotsky, Yuval Atzmon, Gal Chechik

Here we describe a new approach to learn with fewer samples, by using additional information that is provided per sample.

Few-Shot Learning General Classification +1

Adaptive Confidence Smoothing for Generalized Zero-Shot Learning

no code implementations CVPR 2019 Yuval Atzmon, Gal Chechik

Specifically, our model consists of three classifiers: A "gating" model that makes soft decisions if a sample is from a "seen" class, and two experts: a ZSL expert, and an expert model for seen classes.

Generalized Zero-Shot Learning

Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning

1 code implementation7 Jun 2018 Yuval Atzmon, Gal Chechik

The soft group structure can be learned from data jointly as part of the model, and can also readily incorporate prior knowledge about groups if available.

Zero-Shot Learning

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