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- W4287236245 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." @default.
- W4287236245 created "2022-07-25" @default.
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- W4287236245 date "2021-04-08" @default.
- W4287236245 modified "2023-09-29" @default.
- W4287236245 title "Video Question Answering with Phrases via Semantic Roles" @default.
- W4287236245 doi "https://doi.org/10.48550/arxiv.2104.03762" @default.
- W4287236245 hasPublicationYear "2021" @default.
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