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Implementation of brand new video augmentation strategy for video action recognition with 3D CNN

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videomix

Implementation of brand new video augmentation strategy for video action recognition with 3D CNN.

Tubemix is like a 'video-in-video' augmentation, and Stackmix is 'video-to-video'. Use videomix.py for implementing Tubemix and Stackmix.

Example

rgb "TableTennis" rgb "Archery"
rgb stackmix rgb tubemix
opt flow-u "TableTennis" opt flow-u "Archery"
opt flow-u stackmix opt flow-u tubemix

Experimental Result

Training on I3D with Stackmix and Tubemix augmentation.

* RCRF represents applying random crop and random flipping Probability of implementing Stackmix or Tubemix is fixed to p=0.5.

I've also explored how beta distribution effect on training accuracy. Figure below shows the PDF of &\lambda$~beta(α,α).

  • UCF-101
Augmentation method hyper-parameter Spatial Stream Temporal Stream
Baseline(RCRF) - 93.23% 91.36%
Tubemix α=8 93.97% 92.23%
Tubemix α=0.4 93.74% 93.02%
Tubemix α=2 93.52% 92.55%
Stackmix α=8 94.23% 92.68%
Stackmix α=0.4 94.29% 93.05%
Stackmix α=2 94.00% 92.84%
Stackmix α=1 93.97% 93.34%
  • HMDB-51
Augmentation method hyper-parameter Spatial Stream Temporal Stream
Baseline(RCRF) - 74.97% 75.62%
Tubemix α=8 74.05% 76.80%
Tubemix α=0.4 74.31% 77.12%
Tubemix α=2 73.79% 76.99%
Tubemix α=1 74.71% 77.06%
Stackmix α=8 73.59% 76.80%
Stackmix α=0.4 74.05% 77.25%
Stackmix α=2 74.58% 76.21%
Stackmix α=1 73.99% 75.88%

Apply on UCF-101, Stackmix and Tubemix could derive performance improvement(+1~2%) with UCF-101 datasets in both streams. However, when applied to HMDB-51, the performance of temporal stream was improved, but the performance of spatial stream was rather reduced.

Future work

  • Speed jittering augmentation is work in progress.

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Implementation of brand new video augmentation strategy for video action recognition with 3D CNN

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