The Loupe: A Plug-and-Play Attention Module for Amplifying Discriminative Features in Vision Transformers
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911693347487744 |
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| author | Sengodan, Naren |
| author_facet | Sengodan, Naren |
| contents | Fine-Grained Visual Classification (FGVC) requires models to focus on subtle, task-relevant regions rather than broad object context. We present The Loupe, a lightweight plug-and-play spatial gating module for hierarchical Vision Transformers. The module is inserted at an intermediate feature stage, predicts a single-channel spatial mask with a small CNN, and uses that mask to reweight feature activations during end-to-end training with a cross-entropy objective and an l1 sparsity term. On CUB-200-2011, The Loupe improves Swin-Base from 88.36% to 91.72% and Swin-Tiny from 85.14% to 88.61%, with under 0.1% additional parameters. Ablations show that the improvement depends on the insertion point and the sparsity regularizer, suggesting that controlled spatial gating is more effective than naive multi-scale masking in this setting. Qualitative results indicate that the learned masks often align with discriminative bird parts, although the module is not a substitute for part-level supervision and can fail under occlusion or fine-grained intra-part differences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Loupe: A Plug-and-Play Attention Module for Amplifying Discriminative Features in Vision Transformers Sengodan, Naren Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Fine-Grained Visual Classification (FGVC) requires models to focus on subtle, task-relevant regions rather than broad object context. We present The Loupe, a lightweight plug-and-play spatial gating module for hierarchical Vision Transformers. The module is inserted at an intermediate feature stage, predicts a single-channel spatial mask with a small CNN, and uses that mask to reweight feature activations during end-to-end training with a cross-entropy objective and an l1 sparsity term. On CUB-200-2011, The Loupe improves Swin-Base from 88.36% to 91.72% and Swin-Tiny from 85.14% to 88.61%, with under 0.1% additional parameters. Ablations show that the improvement depends on the insertion point and the sparsity regularizer, suggesting that controlled spatial gating is more effective than naive multi-scale masking in this setting. Qualitative results indicate that the learned masks often align with discriminative bird parts, although the module is not a substitute for part-level supervision and can fail under occlusion or fine-grained intra-part differences. |
| title | The Loupe: A Plug-and-Play Attention Module for Amplifying Discriminative Features in Vision Transformers |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.16663 |