Rank Pooling for Action Recognition
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|
|Source:||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|01_Fernando_Rank_Pooling_for_Action_2017.pdf||1.16 MB||Adobe PDF||Request a copy|
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