Papers › Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition
Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition
Yaming Wang, Vlad I. Morariu, Larry S. Davis
Compared to earlier multistage frameworks using CNN features, recent end-to-end deep approaches for fine-grained recognition essentially enhance the mid-level learning capability of CNNs. Previous approaches achieve this by introducing an auxiliary network to infuse localization information into the main classification network, or a sophisticated feature encoding method to capture higher order feature statistics. We show that mid-level representation learning can be enhanced within the CNN framework, by learning a bank of convolutional filters that capture class-specific discriminative patches without extra part or bounding box annotations. Such a filter bank is well structured, properly initialized and discriminatively learned through a novel asymmetric multi-stream architecture with convolutional filter supervision and a non-random layer initialization. Experimental results show that our approach achieves state-of-the-art on three publicly available fine-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies and visualizations are provided to understand our approach.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | CUB-200-2011 | DFL-CNN | Accuracy | 87.4 | #25 of 30 | Archive leaderboard | report |
| Fine-Grained Image Classification | FGVC Aircraft | DFB-CNN | Accuracy | 92.0% | #44 of 57 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | DFL-CNN | Accuracy | 93.8% | #60 of 83 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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