MovieNet is a holistic dataset for movie understanding. MovieNet contains 1,100 movies with a large amount of multi-modal data, e.g. trailers, photos, plot descriptions, etc.. Besides, different aspects of manual annotations are provided in MovieNet, including 1.1M characters with bounding boxes and identities, 42K scene boundaries, 2.5K aligned description sentences, 65K tags of place and action, and 92 K tags of cinematic style.

VITA-MLLM/MovieNet-Summary dataset hosted on Hugging Face and contributed by the HF Datasets community

MovieNet-TeViS is a synopsis-storyboard pair dataset to facilitate the Text synopsis to Video Storyboard (TeViS). In the TeViS task, we aim to retrieve an ordered sequence of images from large-scale movie database as video storyboard to visualize an input text synopsis.

MoviePuzzle Dataset

  Introduce

This dataset is based on MovieNet for MoviePuzzle task. We use 228 movie to generate 10031 movie clips. 7048 clips for train, 589 for val, 1178 for in domain test and 1196 for out domain test.

  Download

You can download the full dataset here: https://movienet.github.io/

  Structure

We categorized a dataset of 10031 movie clips into labels ranging from 0 to 10030. Then, we divided this dataset into four subsets: the training… See the full description on the dataset page: https://huggingface.co/datasets/ColorfulAI/MoviePuzzle.

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The MovieQA dataset is a dataset for movie question answering. to evaluate automatic story comprehension from both video and text. The data set consists of almost 15,000 multiple choice question answers obtained from over 400 movies and features high semantic diversity. Each question comes with a set of five highly plausible answers; only one of which is correct. The questions can be answered using multiple sources of information: movie clips, plots, subtitles, and for a subset scripts and DVS.

GroundCap Dataset

  Dataset Summary

GroundCap is a novel grounded image captioning dataset derived from MovieNet, containing 52,350 movie frames with detailed grounded captions. The dataset uniquely features an ID-based system that maintains object identity throughout captions, enables tracking of object interactions, and grounds not only objects but also actions and locations in the scene.

  Dataset Structure





  Data Instances

Each sample in the dataset… See the full description on the dataset page: https://huggingface.co/datasets/daniel3303/GroundCap.

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