You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

CNNs are widely successful in recognizing human actions in videos, albeit with a great cost of computation. This cost is significantly higher in the case of long-range actions, where a video can span up to a few minutes, on average. The goal of this paper is to reduce the computational cost of these CNNs, without sacrificing their performance. We propose VideoEpitoma, a neural network architecture comprising two modules: a timestamp selector and a video classifier. Given a long-range video of thousands of timesteps, the selector learns to choose only a few but most representative timesteps for the video. This selector resides on top of a lightweight CNN such as MobileNet and uses a novel gating module to take a binary decision: consider or discard a video timestep. This decision is conditioned on both the timestep-level feature and the video-level consensus. A heavyweight CNN model such as I3D takes the selected frames as input and performs video classification. Using off-the-shelf video classifiers, VideoEpitoma reduces the computation by up to 50\% without compromising the accuracy. In addition, we show that if trained end-to-end, the selector learns to make better choices to the benefit of the classifier, despite the selector and the classifier residing on two different CNNs. Finally, we report state-of-the-art results on two datasets for long-range action recognition: Charades and Breakfast Actions, with much-reduced computation. In particular, we match the accuracy of I3D by using less than half of the computation.

Output:
Efficient video classification using frame-based conditional gating module for selecting most-dominant frames, followed by temporal modeling and classifier.