6 papers
Coarse-to-Fine Hierarchical Alignment for UAV-based Human Detection using Diffusion Models
Wenda Li, Meng Wu, Liangzhao Chen +3
Training object detectors demands extensive, task-specific annotations, yet this requirement becomes impractical in UAV-based human detection due to constantly shifting target dist…
Generalization of Diffusion Models Arises with a Balanced Representation Space
Zekai Zhang, Xiao Li, Xiang Li +4
Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective. We analyze the distinctions betwe…
Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling
Xiao Li, Zekai Zhang, Xiang Li +4
Diffusion models, though originally designed for generative tasks, have demonstrated impressive self-supervised representation learning capabilities. A particularly intriguing phen…
Towards Understanding the Mechanisms of Classifier-Free Guidance
Xiang Li, Rongrong Wang, Qing Qu
Classifier-free guidance (CFG) is a core technique powering state-of-the-art image generation systems, yet its underlying mechanisms remain poorly understood. In this work, we begi…
Analyzing and Mitigating Model Collapse in Rectified Flow Models
Huminhao Zhu, Fangyikang Wang, Tianyu Ding +2
Training with synthetic data is becoming increasingly inevitable as synthetic content proliferates across the web, driven by the remarkable performance of recent deep generative mo…
Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure
Xiang Li, Yixiang Dai, Qing Qu
In this work, we study the generalizability of diffusion models by looking into the hidden properties of the learned score functions, which are essentially a series of deep denoise…