3 accepted papers
Diffusion-based generative modeling has been achieving state-of-the-art results on various generation tasks. Most diffusion models, however, are limited to a single-generation modeling. Can we generalize diffusion models with the ability of multi-modal generative training for more generalizable mode…
In this paper we propose VidLA an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First they do not capture both short-range and long-range temporal dependencies and typically employ complex hierarchical deep network ar…
In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. On entity-rich textual resources like Wikipedia, Knowledge-Enhanced PLMs (KEPLMs) incorporate the interactions between tokens and men…