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 …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

We present GLIPv2, a grounded VL understanding model, that serves both localization tasks
(eg, object detection, instance segmentation) and Vision-Language (VL) understanding …

Pre-trained representations are becoming crucial for many NLP and perception tasks. While
representation learning in NLP has transitioned to training on raw text without human …

Generating shapes using natural language can enable new ways of imagining and creating
the things around us. While significant recent progress has been made in text-to-image …

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

Image-text matching, as a fundamental task, bridges the gap between vision and language.
The key of this task is to accurately measure similarity between these two modalities. Prior …

In this work, we propose TediGAN, a novel framework for multi-modal image generation and
manipulation with textual descriptions. The proposed method consists of three components …

The task of retrieving video content relevant to natural language queries plays a critical role
in effectively handling internet-scale datasets. Most of the existing methods for this caption-to …