Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods

By Aditya Mogadala, Marimuthu Kalimuthu, Dietrich Klakow. 2019

148 citations recorded by the source corpus.

Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as machine learning, computer vision, and natural language processing. Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks. This has created significant interest in the integration of vision and language. In this survey, we focus on ten prominent tasks that integrate language and vision by discussing their problem formulation, methods, existing datasets, evaluation measures, and compare the results obtained with corresponding state-of-the-art methods. Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video. Furthermore, we also provide some potential future directions in this field of research with an anticipation that this survey stimulates innovative thoughts and ideas to address the existing challenges and build new applications.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Multimodal Learning

Citations in the corpus

Describe What to Change: A Text-guided Unsupervised Image-to-image Translation Approach (2020, background): “Progressive Manipulation A few state of the art approaches for text-to-image generation and image manipulation are expressly designed for conversational systems [6, 11, 12, 28, 37].”
Recent Advances and Trends in Multimodal Deep Learning: A Review (2021, methodology): “As mentioned earlier, Mogadala et al. [101] explained different models of MMDL using image, video and text modalities.”
Adversarial Text-to-Image Synthesis: A Review (2021, result): “In contrast to previous surveys and reviews [20, 21, 22, 23, 24], this review specifically focuses on the development and evaluation of T2I methods.”