# iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection Source: [iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:50:02.046Z Authors: Huahui Yi, Wei Xu, Ziyuan Qin, Xi Chen, Xiaohu Wu, Kang Li, Qicheng Lao Published: 2025-05-31T05:53:13.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()