MSR-VTT-it Dataset

A large scale dataset for Video Captioning in Italian

MST-VTT is a large-scale video benchmark for video understanding. It contains 200K clip-sentence pairs. MSR-VTT provides 10K web video clips where every video has 20 human-written annotations in English.

MSR-VTT-it is derived from the MSR-VTT dataset and it is obtained through semi-automatic translation of the dataset into Italian. It represents a large-scale dataset for video understanding (captioning) in Italian. The dataset contains 200K video-caption pairs derived from the original English dataset. Following the standard data split in the Microsoft2016 challenge, we use 6,513 videos for training, 497 videos for validation and the remained 2,990 for testing. Another version of the dataset contains only 100 test videos annotated manually.

The MSR-VTT-it dataset is composed of 2 files:

DATASET UNVALIDATED

• videodatainfoITALIANO_2017_6513_497_2990.json: it consists of the annotations for the video from the original full MSR-VTT dataset annotations file, 6,513 videos for training, 497 videos for validation and the remained 2,990 for testing.

DATASET VALIDATED

• videodatainfoITALIANO_2017_6513_497_100_TestValidati.json: it consists of the annotations for the video from the original not-full MSR-VTT dataset annotations file, 6,513 videos for training, 497 videos for validation and 100 for testing.

More details about MSR-VTT-it can be found at this link.

How to cite MSR-VTT-it

This dataset was introduced in the work "Large scale datasets for Image and Video Captioning in Italian" available at the following link. If you find MSR-VTT-it useful for your research, please cite the following paper:

@article{IJCOL:scaiella_et_al:2019,
	author = {Scaiella, Antonio and Croce, Danilo and Basili, Roberto},
	journal = {Italian Journal of Computational Linguistics},
	Editor = {Roberto Basili and Simonetta Montemagni},
	number = 5,
	pages = {49-60},
	title = {Large scale datasets for Image and Video Captioning in Italian},
	publisher = {Accademia University Press},
	url = {http://www.ai-lc.it/IJCoL/v5n2/IJCOL_5_2_3___scaiella_et_al.pdf},
	volume = 2,
	year = 2019
}

Download the caption

To download the captions from the MSR-VTT-it dataset, you just need to clone this repository

git clone https://github.com/crux82/msr-vtt-it.git

The resource is developed by the Semantic Analytics Group of the University of Roma Tor Vergata.

Video Download

MSR-VTT-it only contains the Italian captions. You can download the videos from the original dataset. Please refer to: MST-VTT

Release format

The same format used in the MSR-VTT dataset is adopted:

{
  "info" : {
    "year" : str,
    "version" : str,
    "description": str,
    "contributor": str,
    "data_created": str
  },
  "videos": {
    "id": int,
    "video_id": str,
    "category": int,
    "url": str,
    "start time": float,
    "end time": float,
    "split": str
  },
  "sentences": {
    "sen_id": int,
    "video_id": str,
    "caption": str
  }
}

Size of MSR-VTT-it

The original MSR-VTT dataset contains the following elements:

	#videos	#captions
training	6 513	130 260
development	497	9 940
test	2990	59 800

The NOT human validated MSR-VTT-it contains the following elements:

	#videos	#captions
training	6 513	130 260
development	497	9 940
test	2990	59 800

The human validated MSR-VTT-it contains the following elements:

	#videos	#captions
training	6 513	130 260
development	497	9 940
test	100	2 000

References

J. Xu, T. Mei, T. Yao, and Y. Rui, \MSR-VTT: A large video description dataset for bridging video and language," in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, 2016, pp. 5288-5296. [Online]. Available: https://doi.org/10.1109/CVPR.2016.571

Contacts

For any questions or suggestions, you can send an e-mail to croce@info.uniroma2.it