Papers › Learning Class Unique Features in Fine-Grained Visual Classification
Learning Class Unique Features in Fine-Grained Visual Classification
Runkai Zheng, Zhijia Yu, Yinqi Zhang, Chris Ding, Hei Victor Cheng, Li Liu
A major challenge in Fine-Grained Visual Classification (FGVC) is distinguishing various categories with high inter-class similarity by learning the feature that differentiate the details. Conventional cross entropy trained Convolutional Neural Network (CNN) fails this challenge as it may suffer from producing inter-class invariant features in FGVC. In this work, we innovatively propose to regularize the training of CNN by enforcing the uniqueness of the features to each category from an information theoretic perspective. To achieve this goal, we formulate a minimax loss based on a game theoretic framework, where a Nash equilibria is proved to be consistent with this regularization objective. Besides, to prevent from a feasible solution of minimax loss that may produce redundant features, we present a Feature Redundancy Loss (FRL) based on normalized inner product between each selected feature map pair to complement the proposed minimax loss. Superior experimental results on several influential benchmarks along with visualization show that our method gives full play to the performance of the baseline model without additional computation and achieves comparable results with state-of-the-art models.
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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 | DenseNet161+MM+FRL | Accuracy | 88.5 | #21 of 30 | Archive leaderboard | report |
| Fine-Grained Image Classification | FGVC Aircraft | DenseNet161+MM+FRL | Accuracy | 94.0 % | #15 of 57 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | DenseNet161+MM+FRL | Accuracy | 95.2% | #20 of 83 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | ResNet-18+MM+FRL | Percentage correct | 95.33 | #137 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | ResNet-18+MM+FRL | Percentage correct | 76.64 | #147 of 211 | Archive leaderboard | report |
| Image Classification | STL-10 | ResNet-18+MM+FRL | Percentage correct | 85.42 | #49 of 117 | 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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