In spite of great success in many image recognition tasks achieved by recent deep models, directly applying them to recognize low-resolution images may suffer from low accuracy due to the missing of informative details during resolution degradation. However, these images are still recognizable for subjects who are familiar with the corresponding high-resolution ones. Inspired by that, we propose a teacher-student learning approach to facilitate low-resolution image recognition via hybrid order relational knowledge distillation. The approach refers to three streams: the teacher stream is pretrained to recognize high-resolution images in high accuracy, the student stream is learned to identify low-resolution images by mimicking the teacher's behaviors, and the extra assistant stream is introduced as bridge to help knowledge transfer across the teacher to the student. To extract sufficient knowledge for reducing the loss in accuracy, the learning of student is supervised with multiple losses, which preserves the similarities in various order relational structures. In this way, the capability of recovering missing details of familiar low-resolution images can be effectively enhanced, leading to a better knowledge transfer. Extensive experiments on metric learning, low-resolution image classification and low-resolution face recognition tasks show the effectiveness of our approach, while taking reduced models.

翻译：尽管近期深度模型在许多图像识别任务中取得了巨大成功，但将其直接应用于低分辨率图像识别时，由于分辨率降低过程中信息细节的缺失，往往会导致识别准确率下降。然而，对于熟悉对应高分辨率图像的观察者而言，这些低分辨率图像仍然具备可识别性。受此启发，我们提出一种师生学习框架，通过混合阶关系知识蒸馏来提升低分辨率图像识别性能。该框架包含三个分支：教师分支经过预训练以实现高分辨率图像的高精度识别；学生分支通过模仿教师行为学习识别低分辨率图像；额外引入的辅助分支作为桥梁，协助知识从教师向学生迁移。为提取充分知识以减少准确率损失，学生分支的学习过程受到多损失函数的监督，这些函数保留了不同阶关系结构中的相似性特征。通过这种方式，系统对熟悉低分辨率图像缺失细节的恢复能力得到有效增强，从而实现更优的知识迁移。在度量学习、低分辨率图像分类及低分辨率人脸识别任务上的大量实验验证了本方法的有效性，同时模型参数量显著降低。