Papers › Learning Semantically Enhanced Feature for Fine-Grained Image Classification
Learning Semantically Enhanced Feature for Fine-Grained Image Classification
Wei Luo, Hengmin Zhang, Jun Li, Xiu-Shen Wei
We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we first achieve the sub-feature semantic by arranging feature channels of a CNN into different groups through channel permutation. Meanwhile, to enhance the discriminability of sub-features, the groups are guided to be activated on object parts with strong discriminability by a weighted combination regularization. Our approach is parameter parsimonious and can be easily integrated into the backbone model as a plug-and-play module for end-to-end training with only image-level supervision. Experiments verified the effectiveness of our approach and validated its comparable performance to the state-of-the-art methods. Code is available at https://github.com/cswluo/SEF
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 | FGVC Aircraft | SEF | Accuracy | 92.1% | #43 of 57 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | SEF | Accuracy | 94.0% | #55 of 83 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Dogs | SEF | Accuracy | 88.8% | #20 of 24 | 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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