论文标题
从一眼到“ gotcha”：互动的面部图像检索和渐进的相关性反馈
From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback
论文作者
论文摘要
面部图像检索在法医研究中起着重要作用，在法医调查中，未经训练的证人试图从大量图像中识别嫌疑犯。但是，由于很难在口头和直接地描述人的面部外观，人们自然会通过指代众所周知的现有图像并将面孔的特定区域与它们进行比较，并且在每次提供完整的比较也很具有挑战性。因此，我们提出了一个端到端框架，以通过证人逐渐提供的相关反馈来检索面部图像，从而在多个回合中剥削了历史信息，以及一种互动且迭代的方法来检索心理图像。在不需要任何额外的注释的情况下，我们的模型可以以少量响应工作为代价应用。我们在\ texttt {celeba}上进行实验，并通过对百分位数进行排名并在最佳设置下取得99 \％来评估性能。由于该主题据我们所知，该主题仍然很少探索，因此我们希望我们的工作能够成为进一步研究的垫脚石。
Facial image retrieval plays a significant role in forensic investigations where an untrained witness tries to identify a suspect from a massive pool of images. However, due to the difficulties in describing human facial appearances verbally and directly, people naturally tend to depict by referring to well-known existing images and comparing specific areas of faces with them and it is also challenging to provide complete comparison at each time. Therefore, we propose an end-to-end framework to retrieve facial images with relevance feedback progressively provided by the witness, enabling an exploitation of history information during multiple rounds and an interactive and iterative approach to retrieving the mental image. With no need of any extra annotations, our model can be applied at the cost of a little response effort. We experiment on \texttt{CelebA} and evaluate the performance by ranking percentile and achieve 99\% under the best setting. Since this topic remains little explored to the best of our knowledge, we hope our work can serve as a stepping stone for further research.
