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

TL;DR

This paper introduces M$^2$Net, a novel mobile network architecture for landmark recognition that leverages geographic information to diversify inference paths, improving accuracy while maintaining efficiency.

Contribution

The paper proposes M$^2$Net, a new network that enhances landmark recognition by promoting inference path diversity using geo-location data, with improved accuracy and comparable complexity.

Findings

M$^2$Net outperforms existing portable networks in recognition accuracy.

The diversity-promoting reward function effectively enhances inference path selection.

Experiments on new geo-tagged landmark datasets validate the approach.

Abstract

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.