Diversifying Joint Vision-Language Tokenization Learning

Autor:	Pahuja, Vardaan, Piergiovanni, AJ, Angelova, Anelia
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Building joint representations across images and text is an essential step for tasks such as Visual Question Answering and Video Question Answering. In this work, we find that the representations must not only jointly capture features from both modalities but should also be diverse for better generalization performance. To this end, we propose joint vision-language representation learning by diversifying the tokenization learning process, enabling tokens that are sufficiently disentangled from each other to be learned from both modalities. We observe that our approach outperforms the baseline models in a majority of settings and is competitive with state-of-the-art methods. Comment: Accepted to Transformers for Vision (T4V) workshop, CVPR 2023; 7 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.03421 Zobrazit plný text záznamu View this record from Arxiv