Title
3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos
Abstract
We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K unlabeled videos in 11 different languages and captures popular short video trends like pranks, fails, romance, comedy expressed via unique audio-visual formats like self-shot videos, reaction videos, lip-synching, self-sung songs, etc. 3MASSIV presents an opportunity for multimodal and multilingual semantic understanding on these unique videos by annotating them for concepts, affective states, media types, and audio language. We present a thorough analysis of 3MASSIV and highlight the variety and unique aspects of our dataset compared to other contemporary popular datasets with strong baselines. We also show how the social media content in 3MASSIV is dynamic and temporal in nature, which can be used for semantic understanding tasks and cross-lingual analysis.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.02039
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Datasets and evaluation, Video analysis and understanding
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Vikram Gupta100.34
Trisha Mittal2184.03
Puneet Mathur300.68
Vaibhav Mishra400.34
Mayank Maheshwari500.34
Aniket Bera614819.81
Debdoot Mukherjee701.35
Dinesh Manocha89551787.40