# Video Question Answering: Datasets, Algorithms and Challenges

Source: [Video Question Answering: Datasets, Algorithms and Challenges](<https://www.sota2.com/research/paper/68fa6dd91e209131cea3c698>)

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

Data updated: 2025-10-26T00:44:11.594Z

Authors: Yaoyao Zhong, Junbin Xiao, Wei Ji, Yicong Li, Weihong Deng, Tat-Seng Chua

Published: 2022-03-02T16:34:09.000Z

## Abstract

Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos. It has earned increasing attention with recent research trends in joint vision and language understanding. Yet, compared with ImageQA, VideoQA is largely underexplored and progresses slowly. Although different algorithms have continually been proposed and shown success on different VideoQA datasets, we find that there lacks a meaningful survey to categorize them, which seriously impedes its advancements. This paper thus provides a clear taxonomy and comprehensive analyses to VideoQA, focusing on the datasets, algorithms, and unique challenges. We then point out the research trend of studying beyond factoid QA to inference QA towards the cognition of video contents, Finally, we conclude some promising directions for future exploration.

## 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/2203.01225>)

[PDF](<https://arxiv.org/pdf/2203.01225.pdf>)