In the realm of facial analysis, accurate landmark detection is crucial for
various applications, ranging from face recognition and expression analysis to
animation. Conventional heatmap or coordinate regression-based techniques,
however, often face challenges in terms of computational burden and
quantization errors. To address these issues, we present the KeyPoint
Positioning System (KeyPosS), a groundbreaking facial landmark detection
framework that stands out from existing methods. For the first time, KeyPosS
employs the True-range Multilateration algorithm, a technique originally used
in GPS systems, to achieve rapid and precise facial landmark detection without
relying on computationally intensive regression approaches. The framework
utilizes a fully convolutional network to predict a distance map, which
computes the distance between a Point of Interest (POI) and multiple anchor
points. These anchor points are ingeniously harnessed to triangulate the POI's
position through the True-range Multilateration algorithm. Notably, the
plug-and-play nature of KeyPosS enables seamless integration into any decoding
stage, ensuring a versatile and adaptable solution. We conducted a thorough
evaluation of KeyPosS's performance by benchmarking it against state-of-the-art
models on four different datasets. The results show that KeyPosS substantially
outperforms leading methods in low-resolution settings while requiring a
minimal time overhead. The code is available at
this https URL

该文介绍了一种基于 True-range Multilateration 算法的面部关键点检测框架 (KeyPoint Positioning System, KeyPosS)，该算法不仅能够准确地快速检测面部关键点，而且具有算力小、精度高等优点。

基于 GPS 真实距离多点定位的即插即用人脸关键点检测

KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired  True-Range Multilateration

Convolutional neural networks for semantic segmentation suffer from low
performance at object boundaries. In medical imaging, accurate representation
of tissue surfaces and volumes is important for tracking of disease biomarkers
such as tissue morphology and shape features. In this work, we propose a novel
distance map derived loss penalty term for semantic segmentation. We propose to
use distance maps, derived from ground truth masks, to create a penalty term,
guiding the network's focus towards hard-to-segment boundary regions. We
investigate the effects of this penalizing factor against cross-entropy, Dice,
and focal loss, among others, evaluating performance on a 3D MRI bone
segmentation task from the publicly available Osteoarthritis Initiative
dataset. We observe a significant improvement in the quality of segmentation,
with better shape preservation at bone boundaries and areas affected by partial
volume. We ultimately aim to use our loss penalty term to improve the
extraction of shape biomarkers and derive metrics to quantitatively evaluate
the preservation of shape.

本文提出了一种新的距离图生成损失项来辅助卷积神经网络在医学图像分割中选择难以分割区域，经实验证明此项可以显著提高分割质量、更好地保存骨骼边界形状特征，有望用于提取形态学生物标志物，并得出定量评估指标。

语义分割的距离图损失惩罚项

Distance Map Loss Penalty Term for Semantic Segmentation

In this work, we demonstrate yet another approach to tackle the amodal
segmentation problem. Specifically, we first introduce a new representation,
namely a semantics-aware distance map (sem-dist map), to serve as our target
for amodal segmentation instead of the commonly used masks and heatmaps. The
sem-dist map is a kind of level-set representation, of which the different
regions of an object are placed into different levels on the map according to
their visibility. It is a natural extension of masks and heatmaps, where modal,
amodal segmentation, as well as depth order information, are all
well-described. Then we also introduce a novel convolutional neural network
(CNN) architecture, which we refer to as semantic layering network, to estimate
sem-dist maps layer by layer, from the global-level to the instance-level, for
all objects in an image. Extensive experiments on the COCOA and D2SA datasets
have demonstrated that our framework can predict amodal segmentation, occlusion
and depth order with state-of-the-art performance.

本研究提出一种语义感知距离映射（sem-dist map）与卷积神经网络（CNN）架构相结合的方法，实现对无模态分割问题的解决，经实验表明，该方法具有最先进的性能。