Deep learning has shown remarkable progress in a wide range of problems. However, efficient training of such models requires large-scale datasets, and getting annotations for such datasets can be challenging and costly. In this work, we explore the use of user-generated freely available labels from web videos for video understanding. We create a benchmark dataset consisting of around 2 million videos with associated user-generated annotations and other meta information. We utilize the collected dataset for action classification and demonstrate its usefulness with existing small-scale annotated datasets, UCF101 and HMDB51. We study different loss functions and two pretraining strategies, simple and self-supervised learning. We also show how a network pretrained on the proposed dataset can help against video corruption and label noise in downstream datasets. We present this as a benchmark dataset in noisy learning for video understanding. The dataset, code, and trained models will be publicly available for future research.
@article{arxiv.2110.06827,
title = {NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels},
author = {Mohit Sharma and Raj Patra and Harshal Desai and Shruti Vyas and Yogesh Rawat and Rajiv Ratn Shah},
journal= {arXiv preprint arXiv:2110.06827},
year = {2021}
}