A Better Baseline for AVA

07/26/2018

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by   Rohit Girdhar, et al.

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We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features - in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9 average AP on the validation set of AVA v2.1, up from 14.5 spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3 of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8 submissions to the AVA challenge at CVPR 2018.