Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning
Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly. We discover that due to the operation of stacking local receptive filed, Convolutional Neural Network causes the discriminative region diffusion in high-level feature maps, which leads to inaccurate discriminative region localization. In this paper, we propose an end-to-end Discriminative Feature-oriented Gaussian Mixture Model (DF-GMM), to address the problem of discriminative region diffusion and find better fine-grained details. Specifically, DF-GMM consists of 1) a low-rank representation mechanism (LRM), which learns a set of low-rank discriminative bases by Gaussian Mixture Model (GMM) in high-level semantic feature maps to improve discriminative ability of feature representation, 2) a low-rank representation reorganization mechanism (LR ^2 M) which resumes the space information corresponding to low-rank discriminative bases to reconstruct the low-rank feature maps. It alleviates the discriminative region diffusion problem and locate discriminative regions more precisely. Extensive experiments verify that DF-GMM yields the best performance under the same settings with the most competitive approaches, in CUB-Bird, Stanford-Cars datasets, and FGVC Aircraft.
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Datasets
CUB-200-2011
Stanford Cars
FGVC-Aircraft
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | CUB-200-2011 | DF-GMM | Accuracy | 88.8% | # 39 | |
| Fine-Grained Image Classification | FGVC Aircraft | DF-GMM | Accuracy | 93.8% | # 14 | |
| Fine-Grained Image Classification | Stanford Cars | DF-GMM | Accuracy | 94.8% | # 25 |
