Cross-domain Multi-modal Few-shot Object Detection via Rich Text

• URL: http://arxiv.org/abs/2403.16188v1
• Date: Sun, 24 Mar 2024 15:10:22 GMT
• Title: Cross-domain Multi-modal Few-shot Object Detection via Rich Text
• Authors: Zeyu Shangguan, Daniel Seita, Mohammad Rostami,
• Abstract summary: Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks. 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.
• Score: 21.36633828492347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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