Real-time multi-person pose estimation presents significant challenges in
balancing speed and precision. While two-stage top-down methods slow down as
the number of people in the image increases, existing one-stage methods often
fail to simultaneously deliver high accuracy and real-time performance. This
paper introduces RTMO, a one-stage pose estimation framework that seamlessly
integrates coordinate classification by representing keypoints using dual 1-D
heatmaps within the YOLO architecture, achieving accuracy comparable to
top-down methods while maintaining high speed. We propose a dynamic coordinate
classifier and a tailored loss function for heatmap learning, specifically
designed to address the incompatibilities between coordinate classification and
dense prediction models. RTMO outperforms state-of-the-art one-stage pose
estimators, achieving 1.1% higher AP on COCO while operating about 9 times
faster with the same backbone. Our largest model, RTMO-l, attains 74.8% AP on
COCO val2017 and 141 FPS on a single V100 GPU, demonstrating its efficiency and
accuracy. The code and models are available at
this https URL

RTMO 是一个一阶姿势估计框架，通过在 YOLO 架构中使用双 1-D 热图来表示关键点，实现了与自顶向下方法相当的准确性，同时保持了较高的速度。它采用了动态坐标分类器和定制的损失函数来解决坐标分类和密集预测模型之间的不兼容性。RTMO 在 COCO val2017 上达到了 74.8% 的平均精度（AP），在单个 V100 GPU 上实现了 141 FPS 的高效和准确性。

RTMO: 高性能一阶段实时多人姿势估计

RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose  Estimation

We propose an approach for adversarial attacks on dense prediction models
(such as object detectors and segmentation). It is well known that the attacks
generated by a single surrogate model do not transfer to arbitrary (blackbox)
victim models. Furthermore, targeted attacks are often more challenging than
the untargeted attacks. In this paper, we show that a carefully designed
ensemble can create effective attacks for a number of victim models. In
particular, we show that normalization of the weights for individual models
plays a critical role in the success of the attacks. We then demonstrate that
by adjusting the weights of the ensemble according to the victim model can
further improve the performance of the attacks. We performed a number of
experiments for object detectors and segmentation to highlight the significance
of the our proposed methods. Our proposed ensemble-based method outperforms
existing blackbox attack methods for object detection and segmentation. Finally
we show that our proposed method can also generate a single perturbation that
can fool multiple blackbox detection and segmentation models simultaneously.
Code is available at this https URL

在本文中，我们提出了一种针对稠密预测模型（例如对象检测器和分割器）的对抗攻击方法和模型集成。我们的模型集成方法通过适当的模型权重归一化和根据受害者模型的加权调整产生更有效的攻击。我们还表明，我们的方法在对象检测和分割方面胜过了现有的黑盒攻击方法，并能够生成一个单一的扰动同时欺骗多个黑盒检测和分割模型。