Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition [article]

Biao Qian, Yang Wang, Zhao Zhang, Richang Hong, Meng Wang, Ling Shao
2019 arXiv   pre-print
Deep convolutional neural networks have largely benefited computer vision tasks. However, the high computational complexity limits their real-world applications. To this end, many methods have been proposed for efficient network learning, and applications in portable mobile devices. In this paper, we propose a novel Moving-Mobile-Network, named M^2Net, for landmark recognition, equipped each landmark image with located geographic information. We intuitively find that M^2Net can essentially
more » ... te the diversity of the inference path (selected blocks subset) selection, so as to enhance the recognition accuracy. The above intuition is achieved by our proposed reward function with the input of geo-location and landmarks. We also find that the performance of other portable networks can be improved via our architecture. We construct two landmark image datasets, with each landmark associated with geographic information, over which we conduct extensive experiments to demonstrate that M^2Net achieves improved recognition accuracy with comparable complexity.
arXiv:1912.00418v1 fatcat:svznrlrcsjgbhn5ihu5niyv7mi