Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection

This paper presents Incremental Vision-Language Object Detection (IVLOD), a novel learning task designed to incrementally adapt pre-trained Vision-Language Object Detection Models (VLODMs) to various specialized domains, while simultaneously preserving their zero-shot generalization capabilities for the generalized domain. To address this new challenge, we present the Zero-interference Reparameterizable Adaptation (ZiRa), a novel method that introduces Zero-interference Loss and reparameterization techniques to tackle IVLOD without incurring a significant increase in memory usage. Comprehensive experiments on COCO and ODinW-13 datasets demonstrate that ZiRa effectively safeguards the zero-shot generalization ability of VLODMs while continuously adapting to new tasks. Specifically, after training on ODinW-13 datasets, ZiRa exhibits superior performance compared to CL-DETR and iDETR, boosting zero-shot generalizability by substantial $\textbf{13. 91}$ and $\textbf{8. 74}$ AP, respectively. Our code is available at https: //github. com/JarintotionDin/ZiRaGroundingDINO.

Authors

• Jieren Deng
• Haojian Zhang Engineering Laboratory for Intelligent Industrial Vision Institute of Automation, Chinese Academy of Sciences
• Kun Ding State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation, Chinese Academy of Sciences
• Jianhua Hu
• Xingxuan Zhang Tsinghua University
• Yunkuan Wang

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