Papers › Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Dimitri Korsch, Paul Bodesheim, Joachim Denzler
Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification, part-based solutions gather additional local information in terms of attentions or parts. We propose a novel classification-specific part estimation that uses an initial prediction as well as back-propagation of feature importance via gradient computations in order to estimate relevant image regions. The subsequently detected parts are then not only selected by a-posteriori classification knowledge, but also have an intrinsic spatial extent that is determined automatically. This is in contrast to most part-based approaches and even to available ground-truth part annotations, which only provide point coordinates and no additional scale information. We show in our experiments on various widely-used fine-grained datasets the effectiveness of the mentioned part selection method in conjunction with the extracted part features.
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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 | NABirds | CS-Parts | Accuracy | 88.5% | #22 of 30 | Archive leaderboard | report |
| Fine-Grained Image Classification | NABirds | CS-Part | Accuracy | 88.5% | #23 of 30 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | CS-Parts | Accuracy | 92.5% | #73 of 83 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | CS-Part | Accuracy | 92.5% | #74 of 83 | Archive leaderboard | report |
| Image Classification | Flowers-102 | CS-Parts | Accuracy | 96.9% | #39 of 52 | 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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