Fine-Grained Visual Classification with Batch Confusion Norm
We introduce a regularization concept based on the proposed Batch Confusion Norm (BCN) to address Fine-Grained Visual Classification (FGVC). The FGVC problem is notably characterized by its two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task. Inspired by the use of pairwise confusion energy as a regularization mechanism, we develop the BCN technique to improve the FGVC learning by imposing class prediction confusion on each training batch, and consequently alleviate the possible overfitting due to exploring image feature of fine details. In addition, our method is implemented with an attention gated CNN model, boosted by the incorporation of Atrous Spatial Pyramid Pooling (ASPP) to extract discriminative features and proper attentions. To demonstrate the usefulness of our method, we report state-of-the-art results on several benchmark FGVC datasets, along with comprehensive ablation comparisons.
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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 | BCN | Accuracy | 89.2% | # 34 | |
| Fine-Grained Image Classification | FGVC Aircraft | BCN | Accuracy | 93.5% | # 17 | |
| Fine-Grained Image Classification | Stanford Cars | BCN | Accuracy | 94.8% | # 25 |
