Yoad Tewel

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I am a research scientist at NVIDIA Research and a Computer Science PhD candidate at Tel-Aviv University, working in the Deep Learning Lab under the supervision of Prof. Lior Wolf.

My research focuses on the intersection of computer vision, natural language processing, and machine learning. In particular, I am exploring ways to leverage text-and-image foundation models for solving zero-shot tasks.

Publications

2025

  1. Image
    Make It Count: Text-to-Image Generation with an Accurate Number of Objects
    Lital Binyamin, Yoad Tewel, Hilit Segev, Eran Hirsch, Royi Rassin, and Gal Chechik
    CVPR, 2025

2024

  1. Image
    Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models
    ICLR, 2025
  2. Image
    Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models
    Dvir Samuel, Barak Meiri, Haggai Maron, Yoad Tewel, Nir Darshan, Shai Avidan, Gal Chechik, and Rami Ben-Ari
    ICLR, 2025
  3. Image
    Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models
    Michael Toker, Ido Galil, Hadas Orgad, Rinon GalYoad TewelGal Chechik, and Yonatan Belinkov
    NAACL, 2025
  4. Image
    Training-Free Consistent Text-to-Image Generation
    Yoad Tewel, Omri Kaduri, Rinon Gal, Yoni Kasten, Lior WolfGal Chechik, and Yuval Atzmon
    SIGGRAPH, 2024

2023

  1. Image
    Key-Locked Rank One Editing for Text-to-Image Personalization
    Yoad TewelRinon GalGal Chechik, and Yuval Atzmon
    SIGGRAPH, 2023
  2. Image
    Zero-Shot Video Captioning with Evolving Pseudo-Tokens
    Yoad Tewel, Yoav Shalev, Roy Nadler, Idan Schwartz, and Lior Wolf
    BMVC, 2023 (Oral)

2022

  1. Image
    What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
    Tal Shaharabany, Yoad Tewel, and Lior Wolf
    NeurIPS, 2022
  2. Image
    ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic
    Yoad Tewel, Yoav Shalev, Idan Schwartz, and Lior Wolf
    CVPR, 2022