A Better Baseline for AVA

26 Jul 2018  ·  Rohit Girdhar, João Carreira, Carl Doersch, Andrew Zisserman ·

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% for the best RGB 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%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Action Recognition AVA v2.1 I3D w/ RPN + JFT (Kinetics-400 pretraining( mAP (Val) 22.8 # 11
Action Recognition AVA v2.1 I3D w/ RPN (Kinetics-400 pretraining( mAP (Val) 21.9 # 13

Methods