The benefits of synthetic data for action categorization
This addresses data scarcity in video analysis for computer vision researchers, but it is incremental as it builds on existing methods like Temporal Segment Networks.
The paper tackles the problem of limited real video data for action categorization by using synthetic videos to train neural networks, resulting in improved accuracy on HMDB-51 (up to 72.4% from 68.5%) and UCF-101 (up to 52.7% with synthetic data alone).
In this paper, we study the value of using synthetically produced videos as training data for neural networks used for action categorization. Motivated by the fact that texture and background of a video play little to no significant roles in optical flow, we generated simplified texture-less and background-less videos and utilized the synthetic data to train a Temporal Segment Network (TSN). The results demonstrated that augmenting TSN with simplified synthetic data improved the original network accuracy (68.5%), achieving 71.8% on HMDB-51 when adding 4,000 videos and 72.4% when adding 8,000 videos. Also, training using simplified synthetic videos alone on 25 classes of UCF-101 achieved 30.71% when trained on 2500 videos and 52.7% when trained on 5000 videos. Finally, results showed that when reducing the number of real videos of UCF-25 to 10% and combining them with synthetic videos, the accuracy drops to only 85.41%, compared to a drop to 77.4% when no synthetic data is added.