YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset
Donglai Wei, Siddhant Kharbanda, Sarthak Arora, Roshan Roy, Nishant Jain, Akash Palrecha, Tanav Shah, Shray Mathur
Abstract
Many video understanding tasks require analyzing multi-shot videos, but existing datasets for video object segmentation (VOS) only consider single-shot videos. To address this challenge, we collected a new dataset---YouMVOS---of 200 popular YouTube videos spanning ten genres, where each video is on average five minutes long and with 75 shots. We selected recurring actors and annotated 431K segmentation masks at a frame rate of six, exceeding previous datasets in average video duration, object variation, and narrative structure complexity. We incorporated good practices of model architecture design, memory management, and multi-shot tracking into an existing video segmentation method to build competitive baseline methods. Through error analysis, we found that these baselines still fail to cope with cross-shot appearance variation on our YouMVOS dataset. Thus, our dataset poses new challenges in multi-shot segmentation towards better video analysis. Data, code, and pre-trained models are available at https://donglaiw.github.io/proj/youMVOS
BibTeX
@inproceedings{cvpr2022_youmvosanactorce,
title = {YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset},
author = {Donglai Wei and Siddhant Kharbanda and Sarthak Arora and Roshan Roy and Nishant Jain and Akash Palrecha and Tanav Shah and Shray Mathur and Ritik Mathur and Abhijay Kemkar and Anirudh Chakravarthy and Zudi Lin and Won-Dong Jang and Yansong Tang and Song Bai and James Tompkin and Philip H.S. Torr and Hanspeter Pfister},
booktitle = {CVPR 2022},
year = {2022}
}