In recent years, we have witnessed significant performance boost in the image captioning
task based on vision-language pre-training (VLP). Scale is believed to be an important factor …

Recent advances in image captioning have focused on scaling the data and model size,
substantially increasing the cost of pre-training and finetuning. As an alternative to large …

Image captioning models have achieved impressive results on datasets containing limited
visual concepts and large amounts of paired image-caption training data. However, if these …

The limited availability of annotated data often hinders real-world applications of machine
learning. To efficiently learn from small quantities of multimodal data, we leverage the …

The advent of vision-language pre-training techniques enhanced substantial progress in the
development of models for image captioning. However, these models frequently produce …

Image captioning has received significant attention with remarkable improvements in recent
advances. Nevertheless, images in the wild encapsulate rich knowledge and cannot be …

We introduce a novel framework for image captioning that can produce natural language
explicitly grounded in entities that object detectors find in the image. Our approach …

We do not speak word by word from scratch; our brain quickly structures a pattern like sth do
sth at someplace and then fill in the detailed description. To render existing encoder …

Tremendous progress has been made in recent years in developing better image captioning
models, yet most of them rely on a separate object detector to extract regional features …

Image captioning is a fundamental task in vision-language understanding, where the model
predicts a textual informative caption to a given input image. In this paper, we present a …