activity
20222025
most citedEmu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

30 citations · 35 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2025

Movie Weaver: Tuning-Free Multi-Concept Video Personalization with Anchored Prompts

Feng Liang, Haoyu Ma, Zecheng He +10

Video personalization, which generates customized videos using reference images, has gained significant attention. However, prior methods typically focus on single-concept personal…

cs.CV2024

An Analysis on Quantizing Diffusion Transformers

Yuewei Yang, Jialiang Wang, Xiaoliang Dai +2

Diffusion Models (DMs) utilize an iterative denoising process to transform random noise into synthetic data. Initally proposed with a UNet structure, DMs excel at producing images…

cs.CV20243 cited

Cache Me if You Can: Accelerating Diffusion Models through Block Caching

Felix Wimbauer, Bichen Wu, Edgar Schoenfeld +11

Diffusion models have recently revolutionized the field of image synthesis due to their ability to generate photorealistic images. However, one of the major drawbacks of diffusion…

cs.CV202330 cited

Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23

Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…

cs.IR2023

Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph Pruning and Intent Graph for Effective Recommendations

Zhihang Yu, Shu Wang, Yunqiang Zhu +3

The recommendation of appropriate development pathways, also known as ecological civilization patterns for achieving Sustainable Development Goals (namely, sustainable development…

cs.CV2023

Trainable Projected Gradient Method for Robust Fine-tuning

Junjiao Tian, Xiaoliang Dai, Chih-Yao Ma +3

Recent studies on transfer learning have shown that selectively fine-tuning a subset of layers or customizing different learning rates for each layer can greatly improve robustness…