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

Source: [Recent Advances in Video Question Answering: A Review of Datasets and Methods](<https://www.sota2.com/research/paper/68fa69951e209131cea22d64>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:03:46.301Z

Authors: Devshree Patel, Ratnam Parikh, Yesha Shastri

Published: 2021-01-15T03:26:24.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.05954>)

[PDF](<https://arxiv.org/pdf/2101.05954.pdf>)