Self-supervised landmark estimation is a challenging task that demands the
formation of locally distinct feature representations to identify sparse facial
landmarks in the absence of annotated data. To tackle this task, existing
state-of-the-art (SOTA) methods (1) extract coarse features from backbones that
are trained with instance-level self-supervised learning (SSL) paradigms, which
neglect the dense prediction nature of the task, (2) aggregate them into
memory-intensive hypercolumn formations, and (3) supervise lightweight
projector networks to naively establish full local correspondences among all
pairs of spatial features. In this paper, we introduce SCE-MAE, a framework
that (1) leverages the MAE, a region-level SSL method that naturally better
suits the landmark prediction task, (2) operates on the vanilla feature map
instead of on expensive hypercolumns, and (3) employs a Correspondence
Approximation and Refinement Block (CARB) that utilizes a simple density peak
clustering algorithm and our proposed Locality-Constrained Repellence Loss to
directly hone only select local correspondences. We demonstrate through
extensive experiments that SCE-MAE is highly effective and robust,
outperforming existing SOTA methods by large margins of approximately 20%-44%
on the landmark matching and approximately 9%-15% on the landmark detection
tasks.

SCE-MAE 是一个高度有效且稳健的框架，在标记数据不可用的情况下，通过使用 MAE 方法、基于特征图进行操作以及使用密度峰聚类算法和局部受限排斥损失来直接提取部分局部对应关系，大幅超越了现有 SOTA 方法约 20%-44% 的地标匹配和约 9%-15% 的地标检测任务。

SCE-MAE：用掩蔽自编码器进行选择性对应增强的自监督地标估计

SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder  for Self-Supervised Landmark Estimation

Automatic vertebra localization and identification in CT scans is important
for numerous clinical applications. Much progress has been made on this topic,
but it mostly targets positional localization of vertebrae, ignoring their
orientation. Additionally, most methods employ heuristics in their pipeline
that can be sensitive in real clinical images which tend to contain
abnormalities. We introduce a simple pipeline that employs a standard
prediction with a U-Net, followed by a single graph neural network to associate
and classify vertebrae with full orientation. To test our method, we introduce
a new vertebra dataset that also contains pedicle detections that are
associated with vertebra bodies, creating a more challenging landmark
prediction, association and classification task. Our method is able to
accurately associate the correct body and pedicle landmarks, ignore false
positives and classify vertebrae in a simple, fully trainable pipeline avoiding
application-specific heuristics. We show our method outperforms traditional
approaches such as Hungarian Matching and Hidden Markov Models. We also show
competitive performance on the standard VerSe challenge body identification
task.

用于 CT 扫描的自动椎骨定位和识别对于许多临床应用很重要，本论文提出了一种简单的流程，使用 U-Net 进行标准预测，然后使用单个图神经网络来关联和分类具有完整方向的椎骨，并通过引入包含骨体关联的脊柱枢等标记来测试我们的方法，展示了我们的方法优于匈牙利匹配和隐马尔可夫模型的传统方法，在标准 VerSe 挑战的骨体识别任务上也表现出有竞争力的性能。

利用同时节点和边预测的图神经网络实现骨椎可靠识别

Robust vertebra identification using simultaneous node and edge  predicting Graph Neural Networks

In this paper, we propose a novel face alignment method that trains deep
convolutional network from coarse to fine. It divides given landmarks into
principal subset and elaborate subset. We firstly keep a large weight for
principal subset to make our network primarily predict their locations while
slightly take elaborate subset into account. Next the weight of principal
subset is gradually decreased until two subsets have equivalent weights. This
process contributes to learn a good initial model and search the optimal model
smoothly to avoid missing fairly good intermediate models in subsequent
procedures. On the challenging COFW dataset [1], our method achieves 6.33% mean
error with a reduction of 21.37% compared with the best previous result [2].

本文提出了一种新的人脸对齐方法，该方法使用深度卷积网络从粗到细进行训练，将关键点分为主要子集和详细子集，并逐渐减小主要子集的权重，使两个子集具有相等的权重。在 COFW 数据集上取得了 6.33% 的平均误差，相较于最佳结果 [2] 减少了 21.37%。