Efficient Masked Attention Transformer for Few-Shot Classification and Segmentation
| datacite.relation.isSupplementTo | https://arxiv.org/abs/2507.23642 | |
| dc.contributor.author | Carrión-Ojeda, Dustin | |
| dc.contributor.author | Roth, Stefan | |
| dc.contributor.author | Schaub-Meyer, Simone | |
| dc.date.accessioned | 2025-08-26T08:28:02Z | |
| dc.date.created | 2025-08-25 | |
| dc.date.issued | 2025-08-26 | |
| dc.description | Few-shot classification and segmentation (FS-CS) focuses on jointly performing multi-label classification and multi-class segmentation using few annotated examples. Although the current state of the art (SOTA) achieves high accuracy in both tasks, it struggles with small objects. To overcome this, we propose the Efficient Masked Attention Transformer (EMAT), which improves classification and segmentation accuracy, especially for small objects. EMAT introduces three modifications: a novel memory-efficient masked attention mechanism, a learnable downscaling strategy, and parameter-efficiency enhancements. EMAT outperforms all FS-CS methods on the PASCAL-5i and COCO-20i datasets, using at least four times fewer trainable parameters. Moreover, as the current FS-CS evaluation setting discards available annotations, despite their costly collection, we introduce two novel evaluation settings that consider these annotations to better reflect practical scenarios. | |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4753 | |
| dc.language.iso | en | |
| dc.rights.license | Apache-2.0 (https://www.apache.org/licenses/LICENSE-2.0) | |
| dc.subject | few-shot learning | |
| dc.subject | efficiency | |
| dc.subject | semantic segmentation | |
| dc.subject | multi-label classification | |
| dc.subject | computer vision | |
| dc.subject.classification | 4.43-05 | |
| dc.subject.ddc | 004 | |
| dc.title | Efficient Masked Attention Transformer for Few-Shot Classification and Segmentation | |
| dc.type | Software | |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | 0000-0001-5322-9130 | |
| person.identifier.orcid | 0000-0001-9002-9832 | |
| person.identifier.orcid | 0000-0001-8644-1074 | |
| tuda.agreements | true | |
| tuda.project | HMWK | 500/10.001-(00111) | 3AI-NWG Schaub-Meyer | |
| tuda.project | HMWK | 500/10.001-(00111) | 3AI - TP Roth | |
| tuda.project | EC/H2020 | 866008 | RED | |
| tuda.unit | TUDa |
Files
Original bundle
1 - 10 of 10
| Name | Description | Size | Format | |
|---|---|---|---|---|
| emat-pascal.zip | Model parameters | 316.21 MB | ZIP-Archivdateien | |
| emat-coco.zip | Model parameters | 316.18 MB | ZIP-Archivdateien | |
| cst-pascal.zip | Model parameters | 326.66 MB | ZIP-Archivdateien | |
| cst-coco.zip | Model parameters | 323.8 MB | ZIP-Archivdateien | |
| cst-large-pascal.zip | Model parameters | 326.92 MB | ZIP-Archivdateien | |
| ematseg-pascal.zip | Model parameters | 315.69 MB | ZIP-Archivdateien | |
| ematseg-coco.zip | Model parameters | 315.63 MB | ZIP-Archivdateien | |
| emat_code.zip | Training and inference code (PyTorch) | 83.28 MB | ZIP-Archivdateien | |
| emat_pascal_predictions.zip | Inference results | 82.87 MB | ZIP-Archivdateien | |
| emat_coco_predictions.zip | Inference results | 96.2 MB | ZIP-Archivdateien |