Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection
<|reference_start|>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 additional inference costs or 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 13.91 and 8.74 AP, respectively.<|reference_end|>
@article{deng2024zero-shot,
    title={Zero-shot Generalizable Incremental Learning for Vision-Language Object
  Detection},
    author={Jieren Deng, Haojian Zhang, Kun Ding, Jianhua Hu, Xingxuan Zhang,
  Yunkuan Wang},
    journal={arXiv preprint arXiv:2403.01680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01680},
    primaryClass={cs.CV}
}