Selectively Informative Description can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization

22 March 2024

Papers citing "Selectively Informative Description can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization"

7 / 7 papers shown

Title
LLM-Enabled Style and Content Regularization for Personalized Text-to-Image Generation Anran Yu Wei Feng Yuhang Zhang Xiang Li Lei Meng Lei Wu X. Meng DiffM 24 0 0 19 Apr 2025
Key-Locked Rank One Editing for Text-to-Image Personalization Yoad Tewel Rinon Gal Gal Chechik Y. Atzmon DiffM 140 168 0 02 May 2023
InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning Jing Shi Wei Xiong Zhe-nan Lin H. J. Jung DiffM 130 279 0 06 Apr 2023
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation Zhengxiong Luo Dayou Chen Yingya Zhang Yan Huang Liangsheng Wang Yujun Shen Deli Zhao Jinren Zhou Tien-Ping Tan DiffM VGen 132 215 0 15 Mar 2023
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models Junnan Li Dongxu Li Silvio Savarese Steven C. H. Hoi VLM MLLM 278 4,244 0 30 Jan 2023
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 326 5,785 0 29 Apr 2021
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 321 75,834 0 18 May 2015