Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition
This work addresses efficient network learning for landmark recognition on portable mobile devices, presenting an incremental improvement over existing methods.
The paper tackles the problem of high computational complexity in deep convolutional neural networks for landmark recognition by proposing M^2Net, which uses geographic information to diversify inference path selection, achieving improved recognition accuracy with comparable complexity on two new datasets.
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 \underline{M}oving-\underline{M}obile-\underline{Net}work, named M$^2$Net, for landmark recognition, equipped each landmark image with located geographic information. We intuitively find that M$^2$Net can essentially promote 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$^2$Net achieves improved recognition accuracy with comparable complexity.