Accurate 3D tracking of hand and fingers movements poses significant
challenges in computer vision. The potential applications span across multiple
domains, including human-computer interaction, virtual reality, industry, and
medicine. While gesture recognition has achieved remarkable accuracy,
quantifying fine movements remains a hurdle, particularly in clinical
applications where the assessment of hand dysfunctions and rehabilitation
training outcomes necessitate precise measurements. Several novel and
lightweight frameworks based on Deep Learning have emerged to address this
issue; however, their performance in accurately and reliably measuring fingers
movements requires validation against well-established gold standard systems.
In this paper, the aim is to validate the handtracking framework implemented by
Google MediaPipe Hand (GMH) and an innovative enhanced version, GMH-D, that
exploits the depth estimation of an RGB-Depth camera to achieve more accurate
tracking of 3D movements. Three dynamic exercises commonly administered by
clinicians to assess hand dysfunctions, namely Hand Opening-Closing, Single
Finger Tapping and Multiple Finger Tapping are considered. Results demonstrate
high temporal and spectral consistency of both frameworks with the gold
standard. However, the enhanced GMH-D framework exhibits superior accuracy in
spatial measurements compared to the baseline GMH, for both slow and fast
movements. Overall, our study contributes to the advancement of hand tracking
technology, the establishment of a validation procedure as a good-practice to
prove efficacy of deep-learning-based hand-tracking, and proves the
effectiveness of GMH-D as a reliable framework for assessing 3D hand movements
in clinical applications.

验证 Google MediaPipe Hand (GMH) 及其改进版本 GMH-D 对 3D 手部运动的准确跟踪的有效性，并证明 GMH-D 在临床应用中评估 3D 手部运动方面的优越性。

临床应用中的手部追踪：Google MediaPipe Hand (GMH) 和深度增强 GMH-D 框架的验证

Hand tracking for clinical applications: validation of the Google  MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks

A growing body of work studies how to answer a question or verify a claim by
generating a natural language "proof": a chain of deductive inferences yielding
the answer based on a set of premises. However, these methods can only make
sound deductions when they follow from evidence that is given. We propose a new
system that can handle the underspecified setting where not all premises are
stated at the outset; that is, additional assumptions need to be materialized
to prove a claim. By using a natural language generation model to abductively
infer a premise given another premise and a conclusion, we can impute missing
pieces of evidence needed for the conclusion to be true. Our system searches
over two fringes in a bidirectional fashion, interleaving deductive
(forward-chaining) and abductive (backward-chaining) generation steps. We
sample multiple possible outputs for each step to achieve coverage of the
search space, at the same time ensuring correctness by filtering low-quality
generations with a round-trip validation procedure. Results on a modified
version of the EntailmentBank dataset and a new dataset called Everyday Norms:
Why Not? show that abductive generation with validation can recover premises
across in- and out-of-domain settings

通过混合演绎（前向）和择优推断（后向）生成步骤进行阴性推断，以验证其他假设的缺失证据，避免致信任错误。

具有不完整信息的自然语言推断

Natural Language Deduction with Incomplete Information

Domain adaptation deals with adapting classifiers trained on data from a
source distribution, to work effectively on data from a target distribution. In
this paper, we introduce the Nonlinear Embedding Transform (NET) for
unsupervised domain adaptation. The NET reduces cross-domain disparity through
nonlinear domain alignment. It also embeds the domain-aligned data such that
similar data points are clustered together. This results in enhanced
classification. To determine the parameters in the NET model (and in other
unsupervised domain adaptation models), we introduce a validation procedure by
sampling source data points that are similar in distribution to the target
data. We test the NET and the validation procedure using popular image datasets
and compare the classification results across competitive procedures for
unsupervised domain adaptation.

本文介绍了一个基于非线性嵌入变换的无监督域自适应方法，通过域对齐降低跨域差异，降低数据分布间的偏移，使得相似的数据点可以聚类在一起，提高了分类的性能，并提出了基于样本抽样的验证过程，以确定无监督域适应模型的参数，最后在多个图像数据集上对比了该方法和其他竞争算法的分类结果。