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20242026
most citedImagine yourself: Tuning-Free Personalized Image Generation

1 citations · 1 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CV2024

Apollo: An Exploration of Video Understanding in Large Multimodal Models

Orr Zohar, Xiaohan Wang, Yann Dubois +9

Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly unders…

cs.CV2024

Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction

Shiyu Zhao, Zhenting Wang, Felix Juefei-Xu +7

Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs)…

cs.CV2024

Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation

Bolin Lai, Felix Juefei-Xu, Miao Liu +8

Text-guided image manipulation has experienced notable advancement in recent years. In order to mitigate linguistic ambiguity, few-shot learning with visual examples has been appli…

cs.CV2024★ 1 cited

Imagine yourself: Tuning-Free Personalized Image Generation

Zecheng He, Bo Sun, Felix Juefei-Xu +14

Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for p…

cs.AI2024

The Llama 3 Herd of Models

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…