11 datasets found

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

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

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

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

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

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...

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

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

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...

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

The dataset used in the paper is MSR-VTT, a large video description dataset for bridging video and language. The dataset contains 10k video clips with length varying from 10 to...