Object-Centric Cropping for Visual Few-Shot Classification
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912514744254464 |
|---|---|
| author | Abdali, Aymane Boguslawski, Bartosz Drumetz, Lucas Gripon, Vincent |
| author_facet | Abdali, Aymane Boguslawski, Bartosz Drumetz, Lucas Gripon, Vincent |
| contents | In the domain of Few-Shot Image Classification, operating with as little as one example per class, the presence of image ambiguities stemming from multiple objects or complex backgrounds can significantly deteriorate performance. Our research demonstrates that incorporating additional information about the local positioning of an object within its image markedly enhances classification across established benchmarks. More importantly, we show that a significant fraction of the improvement can be achieved through the use of the Segment Anything Model, requiring only a pixel of the object of interest to be pointed out, or by employing fully unsupervised foreground object extraction methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Object-Centric Cropping for Visual Few-Shot Classification Abdali, Aymane Boguslawski, Bartosz Drumetz, Lucas Gripon, Vincent Computer Vision and Pattern Recognition Machine Learning In the domain of Few-Shot Image Classification, operating with as little as one example per class, the presence of image ambiguities stemming from multiple objects or complex backgrounds can significantly deteriorate performance. Our research demonstrates that incorporating additional information about the local positioning of an object within its image markedly enhances classification across established benchmarks. More importantly, we show that a significant fraction of the improvement can be achieved through the use of the Segment Anything Model, requiring only a pixel of the object of interest to be pointed out, or by employing fully unsupervised foreground object extraction methods. |
| title | Object-Centric Cropping for Visual Few-Shot Classification |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.00218 |