Question Answering - Groups

Causal-VidQA

This dataset is used in the paper to evaluate the performance of the TranSTR architecture.

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ActivityNet-QA

Video question answering (VideoQA) is an essential task in vision-language understanding, which has attracted numerous research attention recently. Nevertheless, existing works...

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Star: A Benchmark for Situated Reasoning in Real-World Videos

The STAR dataset provides 60K situated reasoning questions based on 22K trimmed situation video clips.

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Agqa: A Benchmark for Compositional Spatio-Temporal Reasoning

The AGQA benchmark is a visual dataset comprising 192M hand-crafted questions about 9.6K videos from the Charades dataset.

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Learning to Predict Situation Hyper-Graphs for Video Question Answering

The SHG-VQA model predicts a situation hyper-graph structure composed of existing actions and relations in the input video.

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KnowIT VQA

A video story question answering dataset containing 24,282 questions about 207 episodes of The Big Bang Theory.

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TVQA

TVQA is a video question answering dataset collected from 6 long-running TV shows from 3 genres. There are 21,793 video clips in total for QA collection, accompanied with...

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Dense-Caption Matching and Frame-Selection Gating for Temporal Localization i...

This paper proposes a video question answering model that effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions.

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MSRVTT-QA

Video question answering (VideoQA) requires systems to understand the visual information and infer an answer for a natural language question from it.

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TGIF-QA

The TGIF-QA dataset consists of 165165 QA pairs chosen from 71741 animated GIFs. To evaluate the spatiotemporal reasoning ability at the video level, TGIF-QA dataset designs...

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VATEX

The dataset used in the paper is a video question answering dataset, which is a large-scale video-language pre-training task.

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11 datasets found