  For fine-grained categorization tasks, videos could serve as a better source
than static images as videos have a higher chance of containing discriminative
patterns. Nevertheless, a video sequence could also contain a lot of redundant
and irrelevant frames. How to locate critical information of interest is a
challenging task. In this paper, we propose a new network structure, known as
Redundancy Reduction Attention (RRA), which learns to focus on multiple
discriminative patterns by sup- pressing redundant feature channels.
Specifically, it firstly summarizes the video by weight-summing all feature
vectors in the feature maps of selected frames with a spatio-temporal soft
attention, and then predicts which channels to suppress or to enhance according
to this summary with a learned non-linear transform. Suppression is achieved by
modulating the feature maps and threshing out weak activations. The updated
feature maps are then used in the next iteration. Finally, the video is
classified based on multiple summaries. The proposed method achieves out-
standing performances in multiple video classification datasets. Further- more,
we have collected two large-scale video datasets, YouTube-Birds and
YouTube-Cars, for future researches on fine-grained video categorization. The
datasets are available at http://www.cs.umd.edu/~chenzhu/fgvc.
