Detect an Object At Once without Fine-tuning
This addresses the challenge of one-shot object detection for computer vision applications, offering a novel approach that avoids the need for fine-tuning, though it appears incremental as it builds on existing deep learning techniques.
The paper tackles the problem of detecting previously unseen objects in images without fine-tuning by introducing a two-phase method that generates a Similarity Density Map and uses a Region Alignment Network to locate objects, achieving state-of-the-art results on MS COCO and PASCAL VOC datasets.
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.