Large language models have shown tremendous performance in a variety of tasks.

In this paper, we introduce the new Aero-Eye dataset that focuses on multi-object relationship modeling in aerial videos.

In this paper, we point out that suboptimal noise-data mapping leads to slow training of diffusion models.

In contrast to V AE-based neural compression, where the (mean) decoder is a deterministic neural network, our decoder is a conditional diffusion model. Our approach thus introduces an additional "content" latent variable on which the reverse diffusion process