论文标题
利用自下而上的注意力和自上而下的关注以获取几杆对象检测
Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection
论文作者
论文摘要
很少有射击对象检测目的是检测几乎没有带注释的示例的对象，这仍然是一个具有挑战性的研究问题。最近的研究表明，自学自上而下的注意机制在对象检测和其他视觉任务中的有效性。但是，自上而下的注意力在提高少量探测器的性能方面的有效性较小。由于训练数据不足，对象探测器无法有效地生成几个示例的注意力图。为了提高少数射击对象探测器的性能和解释性，我们提出了一个细心的几杆对象检测网络（ATTFDNET），以吸引自上而下和自下而上的注意力。自下而上的注意力不合时宜，有助于检测和定位自然显着的对象。我们通过引入两个新颖的损失术语和一个混合少量学习策略，进一步解决了几次镜头检测的特定挑战。实验结果和可视化证明了两种注意力的互补性质及其在少量射击对象检测中的作用。代码可在https://github.com/chenxy99/attfdnet上找到。
Few-shot object detection aims at detecting objects with few annotated examples, which remains a challenging research problem yet to be explored. Recent studies have shown the effectiveness of self-learned top-down attention mechanisms in object detection and other vision tasks. The top-down attention, however, is less effective at improving the performance of few-shot detectors. Due to the insufficient training data, object detectors cannot effectively generate attention maps for few-shot examples. To improve the performance and interpretability of few-shot object detectors, we propose an attentive few-shot object detection network (AttFDNet) that takes the advantages of both top-down and bottom-up attention. Being task-agnostic, the bottom-up attention serves as a prior that helps detect and localize naturally salient objects. We further address specific challenges in few-shot object detection by introducing two novel loss terms and a hybrid few-shot learning strategy. Experimental results and visualization demonstrate the complementary nature of the two types of attention and their roles in few-shot object detection. Codes are available at https://github.com/chenxy99/AttFDNet.
