Pose estimation aims to accurately identify anatomical keypoints in humans
and animals using monocular images, which is crucial for various applications
such as human-machine interaction, embodied AI, and autonomous driving. While
current models show promising results, they are typically trained and tested on
clean data, potentially overlooking the corruption during real-world deployment
and thus posing safety risks in practical scenarios. To address this issue, we
introduce PoseBench, a comprehensive benchmark designed to evaluate the
robustness of pose estimation models against real-world corruption. We
evaluated 60 representative models, including top-down, bottom-up,
heatmap-based, regression-based, and classification-based methods, across three
datasets for human and animal pose estimation. Our evaluation involves 10 types
of corruption in four categories: 1) blur and noise, 2) compression and color
loss, 3) severe lighting, and 4) masks. Our findings reveal that
state-of-the-art models are vulnerable to common real-world corruptions and
exhibit distinct behaviors when tackling human and animal pose estimation
tasks. To improve model robustness, we delve into various design
considerations, including input resolution, pre-training datasets, backbone
capacity, post-processing, and data augmentations. We hope that our benchmark
will serve as a foundation for advancing research in robust pose estimation.
The benchmark and source code will be released at
this https URL

我们引入了 PoseBench，这是一个全面的基准，旨在评估姿势估计模型在面对真实世界的破坏时的健壮性，通过评估 60 种代表性模型，包括自上而下、自下而上、基于热图、基于回归和基于分类的方法，跨三个用于人类和动物姿势估计的数据集，我们的研究发现顶尖模型在面对常见真实世界破坏时易受攻击，并在应对人类和动物姿势估计任务时表现出不同行为。为了提高模型健壮性，我们深入研究了各种设计考虑因素，包括输入分辨率、预训练数据集、骨干网络容量、后处理和数据增强。我们希望我们的基准可以为推动健壮姿势估计研究提供基础。

姿势估计模型在损伤下的鲁棒性基准测试

PoseBench: Benchmarking the Robustness of Pose Estimation Models under  Corruptions

Diagnosis based on medical images, such as X-ray images, often involves
manual annotation of anatomical keypoints. However, this process involves
significant human efforts and can thus be a bottleneck in the diagnostic
process. To fully automate this procedure, deep-learning-based methods have
been widely proposed and have achieved high performance in detecting keypoints
in medical images. However, these methods still have clinical limitations:
accuracy cannot be guaranteed for all cases, and it is necessary for doctors to
double-check all predictions of models. In response, we propose a novel deep
neural network that, given an X-ray image, automatically detects and refines
the anatomical keypoints through a user-interactive system in which doctors can
fix mispredicted keypoints with fewer clicks than needed during manual
revision. Using our own collected data and the publicly available AASCE
dataset, we demonstrate the effectiveness of the proposed method in reducing
the annotation costs via extensive quantitative and qualitative results. A demo
video of our approach is available on our project webpage.

提出了一种使用深度神经网络的自动医学影像识别方法，通过用户交互式系统可以检测和修正医学影像中的解剖关键点，达到减少人工修订成本的目的。