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Spatio-temporal action localization consists of three levels of tasks:spatial localization, action classification, and temporal segmentation. In thiswork, we propose a new Progressive Cross-stream Cooperation (PCSC) framework touse both region proposals and features from one stream (i.e. Flow/RGB) to helpanother stream (i.e. RGB/Flow) to iteratively improve action localizationresults and generate better bounding boxes in an iterative fashion.Specifically, we first generate a larger set of region proposals by combiningthe latest region proposals from both streams, from which we can readily obtaina larger set of labelled training samples to help learn better action detectionmodels. Second, we also propose a new message passing approach to passinformation from one stream to another stream in order to learn betterrepresentations, which also leads to better action detection models. As aresult, our iterative framework progressively improves action localizationresults at the frame level. To improve action localization results at the videolevel, we additionally propose a new strategy to train class-specificactionness detectors for better temporal segmentation, which can be readilylearnt by focusing on "confusing" samples from the same action class.Comprehensive experiments on two benchmark datasets UCF-101-24 and J-HMDBdemonstrate the effectiveness of our newly proposed approaches forspatio-temporal action localization in realistic scenarios.

Improving Action Localization by Progressive Cross-stream Cooperation