Dual-Awareness Attention for Few-Shot Object Detection
This work addresses the problem of low performance in few-shot object detection for computer vision systems, representing a strong specific gain rather than a foundational advancement.
The paper tackles the challenge of few-shot object detection (FSOD) by proposing a Dual-Awareness Attention (DAnA) mechanism, which adaptively interprets support images to guide detection networks, resulting in a 47% performance increase (+6.9 AP) and achieving state-of-the-art results.
While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existing FSOD systems follow FSC approaches, ignoring critical issues such as spatial variability and uncertain representations, and consequently result in low performance. Observing this, we propose a novel \textbf{Dual-Awareness Attention (DAnA)} mechanism that enables networks to adaptively interpret the given support images. DAnA transforms support images into \textbf{query-position-aware} (QPA) features, guiding detection networks precisely by assigning customized support information to each local region of the query. In addition, the proposed DAnA component is flexible and adaptable to multiple existing object detection frameworks. By adopting DAnA, conventional object detection networks, Faster R-CNN and RetinaNet, which are not designed explicitly for few-shot learning, reach state-of-the-art performance in FSOD tasks. In comparison with previous methods, our model significantly increases the performance by 47\% (+6.9 AP), showing remarkable ability under various evaluation settings.