BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912313530908672 |
|---|---|
| author | Lai, Jinxiang Wu, Wenlong Zhan, Jiawei Li, Jian Gao, Bin-Bin Liu, Jun Zhang, Jie Guo, Song |
| author_facet | Lai, Jinxiang Wu, Wenlong Zhan, Jiawei Li, Jian Gao, Bin-Bin Liu, Jun Zhang, Jie Guo, Song |
| contents | Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-quality pseudo masks under the teacher-student framework. Building upon this foundation, we propose a BoxSeg framework involving two novel and general modules named the Quality-Aware Module (QAM) and the Peer-assisted Copy-paste (PC). The QAM obtains high-quality pseudo masks and better measures the mask quality to help reduce the effect of noisy masks, by leveraging the quality-aware multi-mask complementation mechanism. The PC imitates Peer-Assisted Learning to further improve the quality of the low-quality masks with the guidance of the obtained high-quality pseudo masks. Theoretical and experimental analyses demonstrate the proposed QAM and PC are effective. Extensive experimental results show the superiority of our BoxSeg over the state-of-the-art methods, and illustrate the QAM and PC can be applied to improve other models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05137 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation Lai, Jinxiang Wu, Wenlong Zhan, Jiawei Li, Jian Gao, Bin-Bin Liu, Jun Zhang, Jie Guo, Song Computer Vision and Pattern Recognition Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-quality pseudo masks under the teacher-student framework. Building upon this foundation, we propose a BoxSeg framework involving two novel and general modules named the Quality-Aware Module (QAM) and the Peer-assisted Copy-paste (PC). The QAM obtains high-quality pseudo masks and better measures the mask quality to help reduce the effect of noisy masks, by leveraging the quality-aware multi-mask complementation mechanism. The PC imitates Peer-Assisted Learning to further improve the quality of the low-quality masks with the guidance of the obtained high-quality pseudo masks. Theoretical and experimental analyses demonstrate the proposed QAM and PC are effective. Extensive experimental results show the superiority of our BoxSeg over the state-of-the-art methods, and illustrate the QAM and PC can be applied to improve other models. |
| title | BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.05137 |