18 datasets found

A dataset for fill-in-the-blank tasks, including video question answering.

NExT-GQA is an extension of NExT-QA with 10.5K temporal grounding (or location) labels tied to the original QA pairs.

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

The HealthVidQA-Prompt dataset is a large-scale medical instructional video question-answering dataset. It contains 52,771 video-question-answer triplets from 13,990 medical...

The HealthVidQA-CRF dataset is a large-scale medical instructional video question-answering dataset. It contains 23,434 video-question-answer triplets from 11,708 medical videos.

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

A dataset for video question answering, including video evidence selection.

Progressive Graph Attention Network for Video Question Answering.

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...

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

Video-Language Models (VLMs), powered by the advancements in Large Language Models (LLMs), are charting new frontiers in video understanding. A pivotal challenge is the...

Video question answering task, which requires machines to answer questions about videos in a natural language form.

The MSVD-QA dataset is a benchmark for video question answering, containing 1,970 videos with multiple-choice questions.

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...

Zero-shot video question answering via frozen bidirectional language models.

A video question answering dataset that focuses on visually grounded video question answering.

Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most state-of-the-art TVR methods learn image-to-video transfer learning...

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...