Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision

Autor:	Shin, Andrew, Ishii, Masato, Narihira, Takuya
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Transformer architectures have brought about fundamental changes to computational linguistic field, which had been dominated by recurrent neural networks for many years. Its success also implies drastic changes in cross-modal tasks with language and vision, and many researchers have already tackled the issue. In this paper, we review some of the most critical milestones in the field, as well as overall trends on how transformer architecture has been incorporated into visuolinguistic cross-modal tasks. Furthermore, we discuss its current limitations and speculate upon some of the prospects that we find imminent. Comment: Accepted for publication by International Journal of Computer Vision (IJCV)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.04037 Zobrazit plný text záznamu View this record from Arxiv