# Video Question Answering with Phrases via Semantic Roles

Source: [Video Question Answering with Phrases via Semantic Roles](<https://www.sota2.com/research/paper/68fa69b01e209131cea236b6>)

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

Data updated: 2025-10-25T23:05:39.232Z

Authors: Arka Sadhu, Kan Chen, Ram Nevatia

Published: 2021-04-08T13:27:43.000Z

## Abstract

Video Question Answering (VidQA) evaluation metrics have been limited to a single-word answer or selecting a phrase from a fixed set of phrases. These metrics limit the VidQA models' application scenario. In this work, we leverage semantic roles derived from video descriptions to mask out certain phrases, to introduce VidQAP which poses VidQA as a fill-in-the-phrase task. To enable evaluation of answer phrases, we compute the relative improvement of the predicted answer compared to an empty string. To reduce the influence of language bias in VidQA datasets, we retrieve a video having a different answer for the same question. To facilitate research, we construct ActivityNet-SRL-QA and Charades-SRL-QA and benchmark them by extending three vision-language models. We further perform extensive analysis and ablative studies to guide future work.

## 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/2104.03762>)

[PDF](<https://arxiv.org/pdf/2104.03762.pdf>)