Cross-domain Multi-modal Few-shot Object Detection via Rich Text
<|reference_start|>Cross-domain Multi-modal Few-shot Object Detection via Rich Text: Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks due to generating richer features. However, existing multi-modal object detection (MM-OD) methods degrade when facing significant domain-shift and are sample insufficient. We hypothesize that rich text information could more effectively help the model to build a knowledge relationship between the vision instance and its language description and can help mitigate domain shift. Specifically, we study the Cross-Domain few-shot generalization of MM-OD (CDMM-FSOD) and propose a meta-learning based multi-modal few-shot object detection method that utilizes rich text semantic information as an auxiliary modality to achieve domain adaptation in the context of FSOD. Our proposed network contains (i) a multi-modal feature aggregation module that aligns the vision and language support feature embeddings and (ii) a rich text semantic rectify module that utilizes bidirectional text feature generation to reinforce multi-modal feature alignment and thus to enhance the model's language understanding capability. We evaluate our model on common standard cross-domain object detection datasets and demonstrate that our approach considerably outperforms existing FSOD methods.<|reference_end|>
@article{shangguan2024cross-domain,
    title={Cross-domain Multi-modal Few-shot Object Detection via Rich Text},
    author={Zeyu Shangguan, Daniel Seita, Mohammad Rostami},
    journal={arXiv preprint arXiv:2403.16188},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16188},
    primaryClass={cs.CV}
}