Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00406 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918041589121024 |
|---|---|
| author | Yi, Huahui Xu, Wei Qin, Ziyuan Chen, Xi Wu, Xiaohu Li, Kang Lao, Qicheng |
| author_facet | Yi, Huahui Xu, Wei Qin, Ziyuan Chen, Xi Wu, Xiaohu Li, Kang Lao, Qicheng |
| contents | Existing prompt-based approaches have demonstrated impressive performance in continual learning, leveraging pre-trained large-scale models for classification tasks; however, the tight coupling between foreground-background information and the coupled attention between prompts and image-text tokens present significant challenges in incremental medical object detection tasks, due to the conceptual gap between medical and natural domains. To overcome these challenges, we introduce the \method~framework, which comprises two main components: 1) Instance-level Prompt Generation (\ipg), which decouples fine-grained instance-level knowledge from images and generates prompts that focus on dense predictions, and 2) Decoupled Prompt Attention (\dpa), which decouples the original prompt attention, enabling a more direct and efficient transfer of prompt information while reducing memory usage and mitigating catastrophic forgetting. We collect 13 clinical, cross-modal, multi-organ, and multi-category datasets, referred to as \dataset, and experiments demonstrate that \method~outperforms existing SOTA methods, with FAP improvements of 5.44\%, 4.83\%, 12.88\%, and 4.59\% in full data, 1-shot, 10-shot, and 50-shot settings, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00406 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection Yi, Huahui Xu, Wei Qin, Ziyuan Chen, Xi Wu, Xiaohu Li, Kang Lao, Qicheng Computer Vision and Pattern Recognition Existing prompt-based approaches have demonstrated impressive performance in continual learning, leveraging pre-trained large-scale models for classification tasks; however, the tight coupling between foreground-background information and the coupled attention between prompts and image-text tokens present significant challenges in incremental medical object detection tasks, due to the conceptual gap between medical and natural domains. To overcome these challenges, we introduce the \method~framework, which comprises two main components: 1) Instance-level Prompt Generation (\ipg), which decouples fine-grained instance-level knowledge from images and generates prompts that focus on dense predictions, and 2) Decoupled Prompt Attention (\dpa), which decouples the original prompt attention, enabling a more direct and efficient transfer of prompt information while reducing memory usage and mitigating catastrophic forgetting. We collect 13 clinical, cross-modal, multi-organ, and multi-category datasets, referred to as \dataset, and experiments demonstrate that \method~outperforms existing SOTA methods, with FAP improvements of 5.44\%, 4.83\%, 12.88\%, and 4.59\% in full data, 1-shot, 10-shot, and 50-shot settings, respectively. |
| title | iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.00406 |