3D (Three-Dimensional) Convolution is a mathematical operation commonly used in signal processing, image analysis, and particularly in the field of computer vision and deep learning. It extends the concept of convolution, which is well-known in 2D image processing, to three dimensions.


Adaptive Feature Abstraction for Translating Video to Text

Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features. However, the variable context-dependent semantics in the video may make it more appropriate to adaptively select features from the multiple CNN layers. We propose a new approach to generating adaptive spatiotemporal representations of videos for the captioning task. A novel attention mechanism is developed, which adaptively and sequentially focuses on different layers of CNN features (levels of feature “abstraction”), as well as local spatiotemporal regions of the feature maps at each layer. The proposed approach is evaluated on three benchmark datasets: YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how the model works, these experiments quantitatively demonstrate the effectiveness of the proposed adaptive spatiotemporal feature abstraction for translating videos to sentences with rich semantics.