2 citations · 3 across the 10 of their papers we have counts for
10 papers
i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu +4
Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state…
UEval: A Benchmark for Unified Multimodal Generation
Bo Li, Yida Yin, Wenhao Chai +2
We introduce UEval, a benchmark to evaluate unified models, i.e., models capable of generating both images and text. UEval comprises 1,000 expert-curated questions that require bot…
Memorization in 3D Shape Generation: An Empirical Study
Shu Pu, Boya Zeng, Kaichen Zhou +2
Generative models are increasingly used in 3D vision to synthesize novel shapes, yet it remains unclear whether their generation relies on memorizing training shapes. Understanding…
SoFlow: Solution Flow Models for One-Step Generative Modeling
Tianze Luo, Haotian Yuan, Zhuang Liu
The multi-step denoising process in diffusion and Flow Matching models causes major efficiency issues, which motivates research on few-step generation. We present Solution Flow Mod…
Stronger Normalization-Free Transformers
Mingzhi Chen, Taiming Lu, Jiachen Zhu +2
Although normalization layers have long been viewed as indispensable components of deep learning architectures, the recent introduction of Dynamic Tanh (DyT) has demonstrated that…
Generative Modeling of Weights: Generalization or Memorization?
Boya Zeng, Yida Yin, Zhiqiu Xu +1
Generative models have recently been explored for synthesizing neural network weights. These approaches take neural network checkpoints as training data and aim to generate high-pe…
Idiosyncrasies in Large Language Models
Mingjie Sun, Yida Yin, Zhiqiu Xu +2
In this work, we unveil and study idiosyncrasies in Large Language Models (LLMs) -- unique patterns in their outputs that can be used to distinguish the models. To do so, we consid…
Understanding Bias in Large-Scale Visual Datasets
Boya Zeng, Yida Yin, Zhuang Liu
A recent study has shown that large-scale visual datasets are very biased: they can be easily classified by modern neural networks. However, the concrete forms of bias among these…
Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference
Zeping Li, Xinlong Yang, Ziheng Gao +7
Large Language Models (LLMs) inherently use autoregressive decoding, which lacks parallelism in inference and results in significantly slow inference speed. While methods such as M…
1st Place Solutions for UG2+ Challenge 2022 ATMOSPHERIC TURBULENCE MITIGATION
Zhuang Liu, Zhichao Zhao, Ye Yuan +3
In this technical report, we briefly introduce the solution of our team ''summer'' for Atomospheric Turbulence Mitigation in UG+ Challenge in CVPR 2022. In this task, we propos…