Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment

• URL: http://arxiv.org/abs/2502.16469v1
• Date: Sun, 23 Feb 2025 06:59:22 GMT
• Title: Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment
• Authors: Zeyu Shangguan, Daniel Seita, Mohammad Rostami,
• Abstract summary: Cross-Domain Multi-Modal Few-Shot Object Detection (CDMM-FSOD)<n>We introduce a meta-learning-based framework designed to leverage rich textual semantics as an auxiliary modality to achieve effective domain adaptation.<n>We evaluate the proposed method on common cross-domain object detection benchmarks and demonstrate that it significantly surpasses existing few-shot object detection approaches.
• Score: 21.36633828492347
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancements in cross-modal feature extraction and integration have significantly enhanced performance in few-shot learning tasks. However, current multi-modal object detection (MM-OD) methods often experience notable performance degradation when encountering substantial domain shifts. We propose that incorporating rich textual information can enable the model to establish a more robust knowledge relationship between visual instances and their corresponding language descriptions, thereby mitigating the challenges of domain shift. Specifically, we focus on the problem of Cross-Domain Multi-Modal Few-Shot Object Detection (CDMM-FSOD) and introduce a meta-learning-based framework designed to leverage rich textual semantics as an auxiliary modality to achieve effective domain adaptation. Our new architecture incorporates two key components: (i) A multi-modal feature aggregation module, which aligns visual and linguistic feature embeddings to ensure cohesive integration across modalities. (ii) A rich text semantic rectification module, which employs bidirectional text feature generation to refine multi-modal feature alignment, thereby enhancing understanding of language and its application in object detection. We evaluate the proposed method on common cross-domain object detection benchmarks and demonstrate that it significantly surpasses existing few-shot object detection approaches.

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