In practical applications of human pose estimation, low-resolution inputs
frequently occur, and existing state-of-the-art models perform poorly with
low-resolution images. This work focuses on boosting the performance of
low-resolution models by distilling knowledge from a high-resolution model.
However, we face the challenge of feature size mismatch and class number
mismatch when applying knowledge distillation to networks with different input
resolutions. To address this issue, we propose a novel cross-domain knowledge
distillation (CDKD) framework. In this framework, we construct a scale-adaptive
projector ensemble (SAPE) module to spatially align feature maps between models
of varying input resolutions. It adopts a projector ensemble to map
low-resolution features into multiple common spaces and adaptively merges them
based on multi-scale information to match high-resolution features.
Additionally, we construct a cross-class alignment (CCA) module to solve the
problem of the mismatch of class numbers. By combining an easy-to-hard training
(ETHT) strategy, the CCA module further enhances the distillation performance.
The effectiveness and efficiency of our approach are demonstrated by extensive
experiments on two common benchmark datasets: MPII and COCO. The code is made
available in supplementary material.

通过知识蒸馏实现不同分辨率模型的性能提升，包括构建一个跨领域知识蒸馏框架，以及使用尺度自适应的投影仪集合模块来空间对齐输入分辨率不同的模型的特征图，进一步通过交叉类别对齐模块和从易到难的训练策略提高蒸馏性能。在 MPII 和 COCO 两个常见基准数据集上进行了大量实验证明了我们方法的有效性和效率。

跨领域知识蒸馏在低分辨率人体姿势估计中的应用

Cross-Domain Knowledge Distillation for Low-Resolution Human Pose  Estimation

In instance-level detection tasks (e.g., object detection), reducing input
resolution is an easy option to improve runtime efficiency. However, this
option traditionally hurts the detection performance much. This paper focuses
on boosting the performance of low-resolution models by distilling knowledge
from a high- or multi-resolution model. We first identify the challenge of
applying knowledge distillation (KD) to teacher and student networks that act
on different input resolutions. To tackle it, we explore the idea of spatially
aligning feature maps between models of varying input resolutions by shifting
feature pyramid positions and introduce aligned multi-scale training to train a
multi-scale teacher that can distill its knowledge to a low-resolution student.
Further, we propose crossing feature-level fusion to dynamically fuse teacher's
multi-resolution features to guide the student better. On several
instance-level detection tasks and datasets, the low-resolution models trained
via our approach perform competitively with high-resolution models trained via
conventional multi-scale training, while outperforming the latter's
low-resolution models by 2.1% to 3.6% in terms of mAP. Our code is made
publicly available at this https URL

使用知识蒸馏技术提高低分辨率模型性能，同时采用对齐多尺度训练方法与特征级融合技术，方法在目标检测任务中表现优异。