论文标题
使用暹罗神经网络检索家庭成员
Retrieval of Family Members Using Siamese Neural Network
论文作者
论文摘要
野外家庭成员的检索旨在在数据集中找到给定主题的家庭成员，这对于找到失去的孩子和分析亲属关系很有用。但是，由于年龄，性别，姿势和收集到的数据的多样性，此任务总是具有挑战性的。为了解决这个问题，我们通过深层暹罗神经网络提出解决方案。我们的解决方案可以分为两个部分：相似性计算和排名。在培训过程中，暹罗网络首先将两个候选图像作为输入，并产生两个特征向量。然后，使用几个完全连接的层计算两个向量之间的相似性。在推理过程中，我们通过删除后面几个完全连接的层并直接计算两个特征向量的余弦相似性来尝试另一种相似性计算方法。经过相似性计算后，我们使用排名算法将相似性得分与相同的身份合并，并根据其相似性输出订购列表。为了获得进一步的改进，我们尝试了主链，训练方法和相似性计算方法的不同组合。最后，我们将最佳组合作为解决方案和我们的团队（USTC-Nelslip）在RFIW2020挑战的曲目中获得了有利的结果，并获得了第一位亚军，这验证了我们方法的有效性。我们的代码可在以下网址找到：https：//github.com/gniknoil/fg2020-kinship
Retrieval of family members in the wild aims at finding family members of the given subject in the dataset, which is useful in finding the lost children and analyzing the kinship. However, due to the diversity in age, gender, pose and illumination of the collected data, this task is always challenging. To solve this problem, we propose our solution with deep Siamese neural network. Our solution can be divided into two parts: similarity computation and ranking. In training procedure, the Siamese network firstly takes two candidate images as input and produces two feature vectors. And then, the similarity between the two vectors is computed with several fully connected layers. While in inference procedure, we try another similarity computing method by dropping the followed several fully connected layers and directly computing the cosine similarity of the two feature vectors. After similarity computation, we use the ranking algorithm to merge the similarity scores with the same identity and output the ordered list according to their similarities. To gain further improvement, we try different combinations of backbones, training methods and similarity computing methods. Finally, we submit the best combination as our solution and our team(ustc-nelslip) obtains favorable result in the track3 of the RFIW2020 challenge with the first runner-up, which verifies the effectiveness of our method. Our code is available at: https://github.com/gniknoil/FG2020-kinship
