Specifically, SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process.

The code is publicly available at https://github.com/jinnh/GSAD.

This prevailing paradigm heavily relies on the out-of-distribution (OOD) effect to influence the model's prediction.

This trend is consistent with similar conclusions in the prior DDPM work [1]. These biased problems should be carefully solved for the deployment of real applications.

To achieve this goal, we provide a generative diffusion-based framework called MomentDiff, which simulates a typical human retrieval process from random browsing to gradual localization. Specifically, we first diffuse the real span to random noise, and learn to denoise the random noise to the original span with the guidance of similarity between text and video.

In DM, kernels play an important role for capturing the nonlinear relationship of data.

Traditional denoising diffusion models are defined on finite-dimension Euclidean spaces (Song and Ermon, 2019; Song et al., 2021; Ho et al., 2020; Dhariwal and Nichol, 2021).