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Beyond Prompt Degradation: Prototype-guided Dual-pool Prompting for Incremental Object Detection

arXiv – CS AI|Yaoteng Zhang, Zhou Qing, Junyu Gao, Qi Wang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose PDP, a new framework for Incremental Object Detection that addresses prompt degradation issues in AI models. The method achieves significant improvements of 9.2% AP on MS-COCO and 3.3% AP on PASCAL VOC benchmarks through dual-pool prompt decoupling and prototype-guided pseudo-label generation.

Key Takeaways

→PDP framework solves prompt degradation problems in incremental object detection through dual-pool prompt decoupling.
→The system separates task-general and task-specific prompts to prevent interference and improve learning efficiency.
→Prototypical Pseudo-Label Generation module maintains consistent supervision signals during incremental learning.
→Method achieves state-of-the-art performance with 9.2% AP improvement on MS-COCO benchmark.
→The approach enables continuous learning of new object categories without forgetting previously learned ones.

#incremental-learning #object-detection #machine-learning #computer-vision #prompt-engineering #continual-learning #ai-research #deep-learning

Read Original →via arXiv – CS AI

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