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. However, existing multi-modal object detection (MM-Olr) 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. Our proposed novel neural network contains (i) a multi-modal feature aggregation module that aligns the vision and language support feature embeddings and (ii) a rich text se-mantic 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 meth-ods. Our implementation is publicly available: https://github.com/zshanggu/CDMM