Automated cephalometric landmark detection is crucial in real-world
orthodontic diagnosis. Current studies mainly focus on only adult subjects,
neglecting the clinically crucial scenario presented by adolescents whose
landmarks often exhibit significantly different appearances compared to adults.
Hence, an open question arises about how to develop a unified and effective
detection algorithm across various age groups, including adolescents and
adults. In this paper, we propose CeLDA, the first work for Cephalometric
Landmark Detection across Ages. Our method leverages a prototypical network for
landmark detection by comparing image features with landmark prototypes. To
tackle the appearance discrepancy of landmarks between age groups, we design
new strategies for CeLDA to improve prototype alignment and obtain a holistic
estimation of landmark prototypes from a large set of training images.
Moreover, a novel prototype relation mining paradigm is introduced to exploit
the anatomical relations between the landmark prototypes. Extensive experiments
validate the superiority of CeLDA in detecting cephalometric landmarks on both
adult and adolescent subjects. To our knowledge, this is the first effort
toward developing a unified solution and dataset for cephalometric landmark
detection across age groups. Our code and dataset will be made public on
this https URL

我们提出了 CeLDA 方法，通过将图像特征与标志物原型进行比较，利用原型网络进行标志物检测，解决了不同年龄组之间标志物出现差异的问题，并引入了新的策略改进原型对齐以获得全面的标志物估计。此外，我们还介绍了一种新的原型关系挖掘范式，用于利用标志物原型之间的解剖关系。大量实验证实了 CeLDA 在成年人和青少年标志物检测方面的优越性，这是首个针对不同年龄组的头影测量标志物检测的统一解决方案和数据集。

原型网络在不同年龄段下的颅面测量点检测

Cephalometric Landmark Detection across Ages with Prototypical Network

The potential for zero-shot generalization in vision-language (V-L) models
such as CLIP has spurred their widespread adoption in addressing numerous
downstream tasks. Previous methods have employed test-time prompt tuning to
adapt the model to unseen domains, but they overlooked the issue of imbalanced
class distributions. In this study, we explicitly address this problem by
employing class-aware prototype alignment weighted by mean class probabilities
obtained for the test sample and filtered augmented views. Additionally, we
ensure that the class probabilities are as accurate as possible by performing
prototype discrimination using contrastive learning. The combination of
alignment and discriminative loss serves as a geometric regularizer, preventing
the prompt representation from collapsing onto a single class and effectively
bridging the distribution gap between the source and test domains. Our method,
named PromptSync, synchronizes the prompts for each test sample on both the
text and vision branches of the V-L model. In empirical evaluations on the
domain generalization benchmark, our method outperforms previous best methods
by 2.33\% in overall performance, by 1\% in base-to-novel generalization, and
by 2.84\% in cross-dataset transfer tasks.

通过分类原型对齐和提示同步来提高视觉语言模型的零样本泛化能力和处理类别不平衡问题，并在测试中取得明显优于其他方法的结果。

PromptSync：通过分类感知的原型对齐和判别消除视觉语言模型中的领域差距

PromptSync: Bridging Domain Gaps in Vision-Language Models through  Class-Aware Prototype Alignment and Discrimination

For named entity recognition (NER) in zero-resource languages, utilizing
knowledge distillation methods to transfer language-independent knowledge from
the rich-resource source languages to zero-resource languages is an effective
means. Typically, these approaches adopt a teacher-student architecture, where
the teacher network is trained in the source language, and the student network
seeks to learn knowledge from the teacher network and is expected to perform
well in the target language. Despite the impressive performance achieved by
these methods, we argue that they have two limitations. Firstly, the teacher
network fails to effectively learn language-independent knowledge shared across
languages due to the differences in the feature distribution between the source
and target languages. Secondly, the student network acquires all of its
knowledge from the teacher network and ignores the learning of target
language-specific knowledge. Undesirably, these limitations would hinder the
model's performance in the target language. This paper proposes an unsupervised
prototype knowledge distillation network (ProKD) to address these issues.
Specifically, ProKD presents a contrastive learning-based prototype alignment
method to achieve class feature alignment by adjusting the distance among
prototypes in the source and target languages, boosting the teacher network's
capacity to acquire language-independent knowledge. In addition, ProKD
introduces a prototypical self-training method to learn the intrinsic structure
of the language by retraining the student network on the target data using
samples' distance information from prototypes, thereby enhancing the student
network's ability to acquire language-specific knowledge. Extensive experiments
on three benchmark cross-lingual NER datasets demonstrate the effectiveness of
our approach.

该研究提出了一种无监督的原型知识蒸馏网络（ProKD）来提高模型在跨语言命名实体识别问题上的表现。该方法通过原型对齐和基于原型的自训练两种方法，让源语言和目标语言的类别特征得到对齐，同时提高模型获取跨语言通用知识和目标语言特定知识的能力。在三个基准数据集上的实验表明该方法的有效性。