S Kornblith, L Li, Z Wang… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
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 …
J Chen, H Guo, K Yi, B Li… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
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 …
Z Luo, Z Hu, Y Xi, R Zhang, J Ma - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
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 …
W Kang, J Mun, S Lee, B Roh - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
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 …