Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition

Sungho Shin, Joosoon Lee, Junseok Lee, Yeonguk Yu, Kyoobin Lee
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引用次数: 8

Abstract

Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention similarity knowledge distillation approach, which transfers attention maps obtained from a high resolution (HR) network as a teacher into an LR network as a student to boost LR recognition performance. Inspired by humans being able to approximate an object's region from an LR image based on prior knowledge obtained from HR images, we designed the knowledge distillation loss using the cosine similarity to make the student network's attention resemble the teacher network's attention. Experiments on various LR face related benchmarks confirmed the proposed method generally improved recognition performances on LR settings, outperforming state-of-the-art results by simply transferring well-constructed attention maps. The code and pretrained models are publicly available in the https://github.com/gist-ailab/teaching-where-to-look.
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教学在哪里看:低分辨率人脸识别的注意力相似知识蒸馏
深度学习在人脸识别基准上取得了出色的表现,但在低分辨率(LR)图像上,性能显著下降。我们提出了一种注意力相似知识蒸馏方法,该方法将作为教师从高分辨率(HR)网络中获得的注意力图转移到作为学生的LR网络中,以提高LR识别性能。受人类能够基于从HR图像中获得的先验知识从LR图像中近似物体区域的启发,我们使用余弦相似度设计了知识蒸馏损失,使学生网络的注意力与教师网络的注意力相似。在各种LR人脸相关基准上的实验证实,所提出的方法在LR设置下总体上提高了识别性能,通过简单地转移构造良好的注意图,优于最先进的结果。代码和预训练模型可在https://github.com/gist-ailab/teaching-where-to-look上公开获得。
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