Recent Advances in Video Question Answering: A Review of Datasets and Methods

By Devshree Patel, Ratnam Parikh, Yesha Shastri. 2021

22 citations recorded by the source corpus.

Video Question Answering (VQA) is a recent emerging challenging task in the field of Computer Vision. Several visual information retrieval techniques like Video Captioning/Description and Video-guided Machine Translation have preceded the task of VQA. VQA helps to retrieve temporal and spatial information from the video scenes and interpret it. In this survey, we review a number of methods and datasets for the task of VQA. To the best of our knowledge, no previous survey has been conducted for the VQA task.

Subfields: 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 Computer Vision and Pattern Recognition

1. Andy Zeng
2. Luc Van Gool
3. Kristen Grauman
4. Dacheng Tao
5. Li Fei-Fei
6. Jitendra Malik
7. Andrew Zisserman
8. Trevor Darrell

In Multimodal Learning

1. Michael S. Ryoo
2. Andy Zeng

Citations in the corpus

• Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language (2022, background): “, re-framing “video Q&A” as a “short story Q&A” problem, which differs from common paradigms for video understanding that may involve supervising videotext models on labeled datasets or adversarial training (see [77] for a recent survey).”