Caricature is an artistic abstraction of the human face by distorting or
exaggerating certain facial features, while still retains a likeness with the
given face. Due to the large diversity of geometric and texture variations,
automatic landmark detection and 3D face reconstruction for caricature is a
challenging problem and has rarely been studied before. In this paper, we
propose the first automatic method for this task by a novel 3D approach. To
this end, we first build a dataset with various styles of 2D caricatures and
their corresponding 3D shapes, and then build a parametric model on vertex
based deformation space for 3D caricature face. Based on the constructed
dataset and the nonlinear parametric model, we propose a neural network based
method to regress the 3D face shape and orientation from the input 2D
caricature image. Ablation studies and comparison with state-of-the-art methods
demonstrate the effectiveness of our algorithm design. Extensive experimental
results demonstrate that our method works well for various caricatures. Our
constructed dataset, source code and trained model are available at
this https URL

本文提出了一种基于 3D 方法的自动化人物卡通画脸部特征检测与重建技术，首先通过建立一个由 2D 卡通图像及其相应的 3D 形状样式构成的数据集，建立了基于顶点的形变空间的参数化模型，然后通过神经网络来回归所需的 3D 脸部形状和方向，并通过消融实验和与最先进的方法进行比较，证明了该算法设计的有效性。

使用非线性参数模型进行夸张画中地标检测与三维人脸重建

Landmark Detection and 3D Face Reconstruction for Caricature using a  Nonlinear Parametric Model

An abdominal ultrasound examination, which is the most common ultrasound
examination, requires substantial manual efforts to acquire standard abdominal
organ views, annotate the views in texts, and record clinically relevant organ
measurements. Hence, automatic view classification and landmark detection of
the organs can be instrumental to streamline the examination workflow. However,
this is a challenging problem given not only the inherent difficulties from the
ultrasound modality, e.g., low contrast and large variations, but also the
heterogeneity across tasks, i.e., one classification task for all views, and
then one landmark detection task for each relevant view. While convolutional
neural networks (CNN) have demonstrated more promising outcomes on ultrasound
image analytics than traditional machine learning approaches, it becomes
impractical to deploy multiple networks (one for each task) due to the limited
computational and memory resources on most existing ultrasound scanners. To
overcome such limits, we propose a multi-task learning framework to handle all
the tasks by a single network. This network is integrated to perform view
classification and landmark detection simultaneously; it is also equipped with
global convolutional kernels, coordinate constraints, and a conditional
adversarial module to leverage the performances. In an experimental study based
on 187,219 ultrasound images, with the proposed simplified approach we achieve
(1) view classification accuracy better than the agreement between two clinical
experts and (2) landmark-based measurement errors on par with inter-user
variability. The multi-task approach also benefits from sharing the feature
extraction during the training process across all tasks and, as a result,
outperforms the approaches that address each task individually.

通过使用多任务学习和卷积神经网络，提出了一种自动分类和检测腹部器官的方法，实验结果表明，该方法能够在单个网络中完成所有任务，提高特征提取的效率以及分类和测量的准确性。

简化即是美：针对腹部超声图像的同时视图分类与标志点检测

Less is More: Simultaneous View Classification and Landmark Detection  for Abdominal Ultrasound Images

In this study, we show that landmark detection or face alignment task is not
a single and independent problem. Instead, its robustness can be greatly
improved with auxiliary information. Specifically, we jointly optimize landmark
detection together with the recognition of heterogeneous but subtly correlated
facial attributes, such as gender, expression, and appearance attributes. This
is non-trivial since different attribute inference tasks have different
learning difficulties and convergence rates. To address this problem, we
formulate a novel tasks-constrained deep model, which not only learns the
inter-task correlation but also employs dynamic task coefficients to facilitate
the optimization convergence when learning multiple complex tasks. Extensive
evaluations show that the proposed task-constrained learning (i) outperforms
existing face alignment methods, especially in dealing with faces with severe
occlusion and pose variation, and (ii) reduces model complexity drastically
compared to the state-of-the-art methods based on cascaded deep model.

本文提出一种新型的任务约束深度模型，通过学习相互关联的异质性面部属性，如性别、表情和外貌属性，与地标检测问题的联合优化，有效提高了地标检测的鲁棒性。通过广泛的评估，表明所提出的任务约束学习方法不仅在处理具有严重遮挡和姿态变化的面部影像方面优于现有的面部对齐方法，而且与基于级联深度模型的现有方法相比，明显减少了模型复杂度。