@inproceedings{
deng2024zeroshot,
title={Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection},
author={Jieren Deng and Haojian Zhang and Kun Ding and Jianhua Hu and Xingxuan Zhang and Yunkuan Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ZNqHm0a35E}
}
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.
Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection
Jieren Deng Haojian Zhang Kun Ding Jianhua Hu Xingxuan Zhang Yunkuan Wang