{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/idpa-instance-decoupled-prompt-attention-for","title":"iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection","arxiv_id":"2506.00406","date":"2025-05-31","proceeding":null,"authors":["Huahui Yi","Wei Xu","Ziyuan Qin","Xi Chen","Xiaohu Wu","Kang Li","Qicheng Lao"],"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.","url_abs":"https://arxiv.org/abs/2506.00406v1","url_pdf":"https://arxiv.org/pdf/2506.00406v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"medical-object-detection","task_name":"Medical Object Detection"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2506.00406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.00406"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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