12 datasets found

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.

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.

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

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...