This paper tackles the challenge of automatically assessing physical
rehabilitation exercises for patients who perform the exercises without
clinician supervision. The objective is to provide a quality score to ensure
correct performance and achieve desired results. To achieve this goal, a new
graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Network with
Transformer, is introduced. This model combines a modified version of STGCN and
transformer architectures for efficient handling of spatio-temporal data. The
key idea is to consider skeleton data respecting its non-linear structure as a
graph and detecting joints playing the main role in each rehabilitation
exercise. Dense connections and GRU mechanisms are used to rapidly process
large 3D skeleton inputs and effectively model temporal dynamics. The
transformer encoder's attention mechanism focuses on relevant parts of the
input sequence, making it useful for evaluating rehabilitation exercises. The
evaluation of our proposed approach on the KIMORE and UI-PRMD datasets
highlighted its potential, surpassing state-of-the-art methods in terms of
accuracy and computational time. This resulted in faster and more accurate
learning and assessment of rehabilitation exercises. Additionally, our model
provides valuable feedback through qualitative illustrations, effectively
highlighting the significance of joints in specific exercises.

本研究使用一种新的基于图的模型，即使用密集时空图卷积 GRU 网络与 Transformer 相结合的模型，对没有临床医生监督下进行的物理康复训练进行自动评估，以提供质量评分确保正确执行并达到期望的效果。在 KIMORE 和 UI-PRMD 数据集上的评估结果表明，与最先进的方法相比，该模型在准确性和计算时间方面具有潜力，实现了更快速和更准确的康复训练学习和评估，并通过定性插图提供有价值的反馈，有效地突出了具体训练中关节的重要性。

D-STGCNT: 基于 Transformer 的稠密时空图卷积 GRU 网络用于患者体能康复评估

D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on  transformer for assessment of patient physical rehabilitation

As the deep learning makes big progresses in still-image face recognition,
unconstrained video face recognition is still a challenging task due to low
quality face images caused by pose, blur, occlusion, illumination etc. In this
paper we propose a network for face recognition which gives an explicit and
quantitative quality score at the same time when a feature vector is extracted.
To our knowledge this is the first network that implements these two functions
in one network online. This network is very simple by adding a quality network
branch to the baseline network of face recognition. It does not require
training datasets with annotated face quality labels. We evaluate this network
on both still-image face datasets and video face datasets and achieve the
state-of-the-art performance in many cases. This network enables a lot of
applications where an explicit face quality scpre is used. We demonstrate three
applications of the explicit face quality, one of which is a progressive
feature aggregation scheme in online video face recognition. We design an
experiment to prove the benefits of using the face quality in this application.
Code will be available at https://github.com/deepcam-cn/facequality.

本文提出了一种基于神经网络的人脸识别质量评分方法，该方法在提取特征向量时给出一个明确的定量质量评分，不需要训练用于注释的人脸质量标签数据集，适用于在线的视频人脸识别。

EQFace：用于人脸识别的简单显式质量网络

EQFace: A Simple Explicit Quality Network for Face Recognition

Crowdsourcing has been part of the IR toolbox as a cheap and fast mechanism
to obtain labels for system development and evaluation. Successful deployment
of crowdsourcing at scale involves adjusting many variables, a very important
one being the number of workers needed per human intelligence task (HIT). We
consider the crowdsourcing task of learning the answer to simple
multiple-choice HITs, which are representative of many relevance experiments.
In order to provide statistically significant results, one often needs to ask
multiple workers to answer the same HIT. A stopping rule is an algorithm that,
given a HIT, decides for any given set of worker answers if the system should
stop and output an answer or iterate and ask one more worker. Knowing the
historic performance of a worker in the form of a quality score can be
beneficial in such a scenario. In this paper we investigate how to devise
better stopping rules given such quality scores. We also suggest adaptive
exploration as a promising approach for scalable and automatic creation of
ground truth. We conduct a data analysis on an industrial crowdsourcing
platform, and use the observations from this analysis to design new stopping
rules that use the workers' quality scores in a non-trivial manner. We then
perform a simulation based on a real-world workload, showing that our algorithm
performs better than the more naive approaches.

本文研究了如何利用质量评分来设计更好的停止规则，同时提出了自适应探索作为一种可扩展且自动创建基准的有前途的方法。通过一个实验模拟，证明了我们的算法比传统方法表现更优。