Training large-scale image captioning (IC) models demands access to a rich and diverse set
of training examples, gathered from the wild, often from noisy alt-text data. However, recent …

Image captioning is conventionally formulated as the task of generating captions that match
the conditional distribution of reference image-caption pairs. However, reference captions in …

We consider the task of image-captioning using only the CLIP model and additional text data
at training time, and no additional captioned images. Our approach relies on the fact that …

Controllable Image Captioning (CIC) aims at generating natural language descriptions for
an image, conditioned on information provided by end users, eg, regions, entities or events …

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 …

Most image captioning frameworks generate captions directly from images, learning a
mapping from visual features to natural language. However, editing existing captions can be …

Image Captioning is a task that requires models to acquire a multimodal understanding of
the world and to express this understanding in natural language text. While the state-of-the …

Image Captioning is a traditional vision-and-language task that aims to generate the
language description of an image. Recent studies focus on scaling up the model size and …

Image captioning is one of the straightforward tasks that can take advantage of large-scale
web-crawled data which provides rich knowledge about the visual world for a captioning …

Training an image captioner without annotated image-sentence pairs has gained traction in
recent years. Previous approaches can be categorized into two strategies: crawling …