无需微调即可一次性检测新物体，像人一样快速识别没见过的物品。
Detect an Object At Once without Fine-tuning
- 用图像卷积生成相似度密度图，定位可能区域
- 通过区域对齐网络精确回归物体位置与范围
- 在COCO和VOC上优于现有方法，无需额外训练
当给出一个或几个未见过物体的图片时，人类能立即在不同场景中识别出该物体。本文提出一种技术实现：首先通过将场景图与目标图块卷积生成相似度密度图（SDM），突出潜在位置；其次利用基于深度孪生网络（DSN）的区域对齐网络（RAN）精确定位物体区域。RAN通过回归真实与预测区域之间的位置和面积差异，实现精准定位。模型通过传统数据集标签预训练，无需微调即可检测新物体。在MS COCO和PASCAL VOC数据集上的实验表明，该方法在相同任务下优于现有最先进方法。
原文摘要 · Abstract (English)
When presented with one or a few photos of a previously unseen object, humans can instantly recognize it in different scenes. Although the human brain mechanism behind this phenomenon is still not fully understood, this work introduces a novel technical realization of this task. It consists of two phases: (1) generating a Similarity Density Map (SDM) by convolving the scene image with the given object image patch(es) so that the highlight areas in the SDM indicate the possible locations; (2) obtaining the object occupied areas in the scene through a Region Alignment Network (RAN). The RAN is constructed on a backbone of Deep Siamese Network (DSN), and different from the traditional DSNs, it aims to obtain the object accurate regions by regressing the location and area differences between the ground truths and the predicted ones indicated by the highlight areas in SDM. By pre-learning from labels annotated in traditional datasets, the SDM-RAN can detect previously unknown objects without fine-tuning. Experiments were conducted on the MS COCO, PASCAL VOC datasets. The results indicate that the proposed method outperforms state-of-the-art methods on the same task.
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