Rank Pooling for Action Recognition
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Fernando, Basura; Gavves, Efstratios; Oramas Mogrovejo, Jose Antonio; Ghodrati, Amir; Tuytelaars, Tinne
Description
We propose a function-based temporal pooling method that captures the latent structure of the video sequence data - e.g., how frame-level features evolve over time in a video. We show how the parameters of a function that has been fit to the video data can serve as a robust new video representation. As a specific example, we learn a pooling function via ranking machines. By learning to rank the frame-level features of a video in chronological order, we obtain a new representation that captures...[Show more]
Collections | ANU Research Publications |
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Date published: | 2017 |
Type: | Journal article |
URI: | http://hdl.handle.net/1885/247695 |
Source: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
DOI: | 10.1109/TPAMI.2016.2558148 |
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01_Fernando_Rank_Pooling_for_Action_2017.pdf | 1.16 MB | Adobe PDF | Request a copy |
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