Human pose estimation is a key task in computer vision with various
applications such as activity recognition and interactive systems. However, the
lack of consistency in the annotated skeletons across different datasets poses
challenges in developing universally applicable models. To address this
challenge, we propose a novel approach integrating multi-teacher knowledge
distillation with a unified skeleton representation. Our networks are jointly
trained on the COCO and MPII datasets, containing 17 and 16 keypoints,
respectively. We demonstrate enhanced adaptability by predicting an extended
set of 21 keypoints, 4 (COCO) and 5 (MPII) more than original annotations,
improving cross-dataset generalization. Our joint models achieved an average
accuracy of 70.89 and 76.40, compared to 53.79 and 55.78 when trained on a
single dataset and evaluated on both. Moreover, we also evaluate all 21
predicted points by our two models by reporting an AP of 66.84 and 72.75 on the
Halpe dataset. This highlights the potential of our technique to address one of
the most pressing challenges in pose estimation research and application - the
inconsistency in skeletal annotations.

通过多教师知识蒸馏与统一的骨骼表示相结合，我们提出了一种新颖的方法来解决姿态估计中不一致的骨骼注释所带来的挑战，同时提升了模型在不同数据集上的适应性，实现了更准确的康复 7 个主要关键点和 21 个扩展关键点的预测。

跨数据集估计人体姿态：一种统一的骨架和多教师蒸馏方法

Estimating Human Poses Across Datasets: A Unified Skeleton and  Multi-Teacher Distillation Approach

Knowledge distillation (KD) has emerged as a promising technique in deep
learning, typically employed to enhance a compact student network through
learning from their high-performance but more complex teacher variant. When
applied in the context of image super-resolution, most KD approaches are
modified versions of methods developed for other computer vision tasks, which
are based on training strategies with a single teacher and simple loss
functions. In this paper, we propose a novel Multi-Teacher Knowledge
Distillation (MTKD) framework specifically for image super-resolution. It
exploits the advantages of multiple teachers by combining and enhancing the
outputs of these teacher models, which then guides the learning process of the
compact student network. To achieve more effective learning performance, we
have also developed a new wavelet-based loss function for MTKD, which can
better optimize the training process by observing differences in both the
spatial and frequency domains. We fully evaluate the effectiveness of the
proposed method by comparing it to five commonly used KD methods for image
super-resolution based on three popular network architectures. The results show
that the proposed MTKD method achieves evident improvements in super-resolution
performance, up to 0.46dB (based on PSNR), over state-of-the-art KD approaches
across different network structures. The source code of MTKD will be made
available here for public evaluation.

我们提出了一种新颖的多教师知识蒸馏（MTKD）框架，专门用于图像超分辨率，通过结合和增强多个教师模型的输出来指导紧凑的学生网络的学习过程，并通过在空间和频率域中观察差异来优化训练过程，从而在超分辨率性能上实现了明显的改善。

MTKD：图像超分辨率的多教师知识蒸馏

MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution

Multi-Teacher knowledge distillation provides students with additional
supervision from multiple pre-trained teachers with diverse information
sources. Most existing methods explore different weighting strategies to obtain
a powerful ensemble teacher, while ignoring the student with poor learning
ability may not benefit from such specialized integrated knowledge. To address
this problem, we propose Adaptive Multi-teacher Knowledge Distillation with
Meta-Learning (MMKD) to supervise student with appropriate knowledge from a
tailored ensemble teacher. With the help of a meta-weight network, the diverse
yet compatible teacher knowledge in the output layer and intermediate layers is
jointly leveraged to enhance the student performance. Extensive experiments on
multiple benchmark datasets validate the effectiveness and flexibility of our
methods. Code is available: this https URL

本文提出了自适应多教师知识蒸馏与元学习，以指导学生从量身定制的集成教师中获得适当的知识。通过元权重网络的辅助，将输出层和中间层中多样但兼容的教师知识联合利用，以增强学生的表现。在多个基准数据集上进行的广泛实验验证了本方法的有效性和灵活性。