Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

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 …

Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory
skills, such as visual understanding, to achieve stronger generic intelligence. In this paper …

We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects.
This approach utilizes pretrained models from image captioning, image-text alignment, and …

Recent advancements in multimodal pre-training have shown promising efficacy in 3D
representation learning by aligning multimodal features across 3D shapes their 2D …

The availability of large-scale image captioning and visual question answering datasets has
contributed significantly to recent successes in vision-and-language pre-training. However …

We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with
FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion …

Transformer-based architectures represent the state of the art in sequence modeling tasks
like machine translation and language understanding. Their applicability to multi-modal …

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

This paper presents a unified Vision-Language Pre-training (VLP) model. The model is
unified in that (1) it can be fine-tuned for either vision-language generation (eg, image …