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Previous models for video captioning often use the output from a specificlayer of a Convolutional Neural Network (CNN) as video features. However, thevariable context-dependent semantics in the video may make it more appropriateto adaptively select features from the multiple CNN layers. We propose a newapproach for generating adaptive spatiotemporal representations of videos forthe captioning task. A novel attention mechanism is developed, that adaptivelyand sequentially focuses on different layers of CNN features (levels of feature"abstraction"), as well as local spatiotemporal regions of the feature maps ateach layer. The proposed approach is evaluated on three benchmark datasets:YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how themodel works, these experiments quantitatively demonstrate the effectiveness ofthe proposed adaptive spatiotemporal feature abstraction for translating videosto sentences with rich semantics.

Adaptive Feature Abstraction for Translating Video to Text