To what extent does replacing GAN-based layout priors with denoising diffusion processes improve the alignment

Abstract

Abstract: Layout Generation aims to synthesize plausible arrangements from given elements. Currently, the predominant methods in layout generation are Generative Adversarial Networks (GANs) and diffusion models, each presenting its own set of challenges. GANs typically struggle with handling discrete data due to their requirement for differentiable generated samples and have historically circumvented the direct generation of discrete labels by treating them as fixed conditions. Conversely, diffusion-based models, despite achieving state-of-the-art performance across several metrics, require extensive sa

Research Question

To what extent does replacing GAN-based layout priors with denoising diffusion processes improve the alignment stability of instruction-tuned models in visual grounding tasks?

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