Challenges and Prospects in Vision and Language Research
Machine Learning
2019-05-28 v2 Computation and Language
Computer Vision and Pattern Recognition
Neural and Evolutionary Computing
Machine Learning
Abstract
Language grounded image understanding tasks have often been proposed as a method for evaluating progress in artificial intelligence. Ideally, these tasks should test a plethora of capabilities that integrate computer vision, reasoning, and natural language understanding. However, rather than behaving as visual Turing tests, recent studies have demonstrated state-of-the-art systems are achieving good performance through flaws in datasets and evaluation procedures. We review the current state of affairs and outline a path forward.
Cite
@article{arxiv.1904.09317,
title = {Challenges and Prospects in Vision and Language Research},
author = {Kushal Kafle and Robik Shrestha and Christopher Kanan},
journal= {arXiv preprint arXiv:1904.09317},
year = {2019}
}