Action Recognition by Weakly-Supervised Discriminative Region Localization

We present a novel probabilistic model for recognizing actions by identifying and extracting information from discriminative regions in videos. The model is trained in a weakly-supervised manner: training videos are annotated only with training label with-out any action location information within the video. Additionally, we eliminate the need for any pre-processing measures to help shortlist candidate action locations. Our local-ization experiments on UCF Sports dataset show that the discriminative regions produced by this weakly supervised system are comparable in quality to action locations produced by systems that require training on datasets with fully annotated location information. Furthermore, our classification experiments on UCF Sports and two other major action recognition benchmark datasets, HMDB and UCF101, show that our recognition system significantly outperforms the baseline models and is comparable to the state-of-the-art.