Connecting Vision and Language plays an essential role in Generative Intelligence. For this
reason, large research efforts have been devoted to image captioning, ie describing images …

Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …

Recently, diffusion models have emerged as a new paradigm for generative models.
Despite the success in domains using continuous signals such as vision and audio …

Humans are able to describe image contents with coarse to fine details as they wish.
However, most image captioning models are intention-agnostic which cannot generate …

In this paper, we propose to guide the video caption generation with Part-of-Speech (POS)
information, based on a gated fusion of multiple representations of input videos. We …

Existing methods on video captioning have made great efforts to identify objects/instances in
videos, but few of them emphasize the prediction of action. As a result, the learned models …

Creating graphic layouts is a fundamental step in graphic designs. In this work, we present a
novel generative model named LayoutDiffusion for automatic layout generation. As layout is …

We address the challenging problem of image captioning by revisiting the representation of
image scene graph. At the core of our method lies the decomposition of a scene graph into a …

Current captioning approaches can describe images using black-box architectures whose
behavior is hardly controllable and explainable from the exterior. As an image can be …

Diffusion models have achieved state-of-the-art synthesis quality on both visual and audio
tasks, and recent works further adapt them to textual data by diffusing on the embedding …