Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition

TL;DR

This paper introduces a lightweight training method for mobile networks that enhances fine-grained recognition by progressively integrating multi-stage features through recursive image mosaics, achieving improved accuracy and robustness.

Contribution

It proposes RMG-PMSI, a novel training approach combining recursive mosaics and multi-stage interaction to boost mobile network performance in fine-grained classification.

Findings

Significant accuracy improvements on benchmark datasets.

Enhanced robustness and transferability of the model.

Effective utilization of multi-stage features in lightweight networks.

Abstract

Fine-grained Visual Classification (FGVC) aims to identify objects from subcategories. It is a very challenging task because of the subtle inter-class differences. Existing research applies large-scale convolutional neural networks or visual transformers as the feature extractor, which is extremely computationally expensive. In fact, real-world scenarios of fine-grained recognition often require a more lightweight mobile network that can be utilized offline. However, the fundamental mobile network feature extraction capability is weaker than large-scale models. In this paper, based on the lightweight MobilenetV2, we propose a Progressive Multi-Stage Interactive training method with a Recursive Mosaic Generator (RMG-PMSI). First, we propose a Recursive Mosaic Generator (RMG) that generates images with different granularities in different phases. Then, the features of different stages pass through a Multi-Stage Interaction (MSI) module, which strengthens and complements the corresponding features of different stages. Finally, using the progressive training (P), the features extracted by the model in different stages can be fully utilized and fused with each other. Experiments on three prestigious fine-grained benchmarks show that RMG-PMSI can significantly improve the performance with good robustness and transferability.