Omni-sourced Webly-supervised Learning for Video Recognition

• URL: http://arxiv.org/abs/2003.13042v2
• Date: Tue, 25 Aug 2020 06:36:16 GMT
• Title: Omni-sourced Webly-supervised Learning for Video Recognition
• Authors: Haodong Duan, Yue Zhao, Yuanjun Xiong, Wentao Liu, Dahua Lin
• Abstract summary: We introduce OmniSource, a framework for leveraging web data to train video recognition models. Experiments show that by utilizing data from multiple sources and formats, OmniSource is more data-efficient in training.
• Score: 74.3637061856504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce OmniSource, a novel framework for leveraging web data to train video recognition models. OmniSource overcomes the barriers between data formats, such as images, short videos, and long untrimmed videos for webly-supervised learning. First, data samples with multiple formats, curated by task-specific data collection and automatically filtered by a teacher model, are transformed into a unified form. Then a joint-training strategy is proposed to deal with the domain gaps between multiple data sources and formats in webly-supervised learning. Several good practices, including data balancing, resampling, and cross-dataset mixup are adopted in joint training. Experiments show that by utilizing data from multiple sources and formats, OmniSource is more data-efficient in training. With only 3.5M images and 800K minutes videos crawled from the internet without human labeling (less than 2% of prior works), our models learned with OmniSource improve Top-1 accuracy of 2D- and 3D-ConvNet baseline models by 3.0% and 3.9%, respectively, on the Kinetics-400 benchmark. With OmniSource, we establish new records with different pretraining strategies for video recognition. Our best models achieve 80.4%, 80.5%, and 83.6 Top-1 accuracies on the Kinetics-400 benchmark respectively for training-from-scratch, ImageNet pre-training and IG-65M pre-training.

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