Jointly Training of Binary 3D CNN Features for Action Recognition

This paper presents a novel method to train the quantized feature with the action recognition task jointly. A quantization and inverse-quantization layers are introduced to the 3D CNN. The quantization and the action recognition loss functions are minimized jointly. That is, the method aims to learn the feature not only to improve action recognition accuracy but also reduce the information loss of the quantization. The framework is shown in Fig. (1).