Deep learning has developed as an effective machine learning method that takes in
numerous layers of features or representation of the data and provides state-of-the-art …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

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 …

In text-video retrieval, the objective is to learn a cross-modal similarity function between a
text and a video that ranks relevant text-video pairs higher than irrelevant pairs. However …

Dense video captioning aims to generate multiple associated captions with their temporal
locations from the video. Previous methods follow a sophisticated" localize-then-describe" …

Temporal modeling is key for action recognition in videos. It normally considers both short-
range motions and long-range aggregations. In this paper, we propose a Temporal …

In this paper, we introduce ActBERT for self-supervised learning of joint video-text
representations from unlabeled data. First, we leverage global action information to catalyze …

Attention Model has now become an important concept in neural networks that has been
researched within diverse application domains. This survey provides a structured and …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Taking full advantage of the information from both vision and language is critical for the
video captioning task. Existing models lack adequate visual representation due to the …