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

arXiv: https://arxiv.org/abs/1912.00418 · HTML: https://mlsanity.com/paper/1912.00418/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Rethinking Data-Free Quantization as a Zero-Sum Game](https://mlsanity.com/paper/2302.09572/) (2023)
- [Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis](https://mlsanity.com/paper/2302.08706/) (2023)
- [Multi-level Adversarial Spatio-temporal Learning for Footstep Pressure based FoG Detection](https://mlsanity.com/paper/2209.10770/) (2022)
- [Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers](https://mlsanity.com/paper/2606.04373/) (2026)

## Abstract (arXiv)

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 underlineMoving-underlineMobile-underlineNetwork, named M2Net, for landmark recognition, equipped each landmark image with located geographic information. We intuitively find that M2Net 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 M2Net achieves improved recognition accuracy with comparable complexity.