3 citations · 5 across the 9 of their papers we have counts for
9 papers
GenScale: A Benchmark for Relative Object Scale in Image Generation and Editing
Lingxiao Li, Max Whitton, Ledell Wu +1
Modern image generation and editing systems can produce photorealistic, prompt-aligned images, but still often render familiar objects at implausible relative sizes. To measure thi…
Moiré Video Authentication: A Physical Signature Against AI Video Generation
Yuan Qing, Kunyu Zheng, Lingxiao Li +2
Recent advances in video generation have made AI-synthesized content increasingly difficult to distinguish from real footage. We propose a physics-based authentication signature th…
Attention to Neural Plagiarism: Diffusion Models Can Plagiarize Your Copyrighted Images!
Zihang Zou, Boqing Gong, Liqiang Wang
In this paper, we highlight a critical threat posed by emerging neural models: data plagiarism. We demonstrate how modern neural models (e.g., diffusion models) can replicate copyr…
Culture in Action: Evaluating Text-to-Image Models through Social Activities
Sina Malakouti, Boqing Gong, Adriana Kovashka
Text-to-image (T2I) diffusion models achieve impressive photorealism by training on large-scale web data, but models inherit cultural biases and fail to depict underrepresented reg…
On Discrete Prompt Optimization for Diffusion Models
Ruochen Wang, Ting Liu, Cho-Jui Hsieh +1
This paper introduces the first gradient-based framework for prompt optimization in text-to-image diffusion models. We formulate prompt engineering as a discrete optimization probl…
Understanding the Impact of Negative Prompts: When and How Do They Take Effect?
Yuanhao Ban, Ruochen Wang, Tianyi Zhou +3
The concept of negative prompts, emerging from conditional generation models like Stable Diffusion, allows users to specify what to exclude from the generated images.%, demonstrati…
The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise
Yuanhao Ban, Ruochen Wang, Tianyi Zhou +3
Diffusion models have achieved remarkable success in text-to-image generation tasks; however, the role of initial noise has been rarely explored. In this study, we identify specifi…
Automatic Jailbreaking of the Text-to-Image Generative AI Systems
Minseon Kim, Hyomin Lee, Boqing Gong +2
Recent AI systems have shown extremely powerful performance, even surpassing human performance, on various tasks such as information retrieval, language generation, and image gener…
Towards A Unified Neural Architecture for Visual Recognition and Reasoning
Calvin Luo, Boqing Gong, Ting Chen +1
Recognition and reasoning are two pillars of visual understanding. However, these tasks have an imbalance in focus; whereas recent advances in neural networks have shown strong emp…