Making top-down human pose estimation method present both good performance
and high efficiency is appealing. Mask RCNN can largely improve the efficiency
by conducting person detection and pose estimation in a single framework, as
the features provided by the backbone are able to be shared by the two tasks.
However, the performance is not as good as traditional two-stage methods. In
this paper, we aim to largely advance the human pose estimation results of
Mask-RCNN and still keep the efficiency. Specifically, we make improvements on
the whole process of pose estimation, which contains feature extraction and
keypoint detection. The part of feature extraction is ensured to get enough and
valuable information of pose. Then, we introduce a Global Context Module into
the keypoints detection branch to enlarge the receptive field, as it is crucial
to successful human pose estimation. On the COCO val2017 set, our model using
the ResNet-50 backbone achieves an AP of 68.1, which is 2.6 higher than Mask
RCNN (AP of 65.5). Compared to the classic two-stage top-down method
SimpleBaseline, our model largely narrows the performance gap (68.1 AP vs. 68.9
AP) with a much faster inference speed (77 ms vs. 168 ms), demonstrating the
effectiveness of the proposed method. Code is available at:
this https URL

本文针对使用 Mask RCNN 进行人体姿态估计时存在的性能问题及效率问题，提出了一种基于改进后的全局上下文模块和 ResNet-50 骨架的人体姿态估计方法，取得了较好的姿态估计效果和运行效率。

面向高性能的单阶段人体姿态估计

Towards High Performance One-Stage Human Pose Estimation

We developed a real-time, high-quality semi-supervised video object
segmentation algorithm. Its accuracy is on par with the most accurate,
time-consuming online-learning model, while its speed is similar to the fastest
template-matching method with sub-optimal accuracy. The core component of the
model is a novel global context module that effectively summarizes and
propagates information through the entire video. Compared to previous
approaches that only use one frame or a few frames to guide the segmentation of
the current frame, the global context module uses all past frames. Unlike the
previous state-of-the-art space-time memory network that caches a memory at
each spatio-temporal position, the global context module uses a fixed-size
feature representation. Therefore, it uses constant memory regardless of the
video length and costs substantially less memory and computation. With the
novel module, our model achieves top performance on standard benchmarks at a
real-time speed.

本研究开发了一种实时、高质量的半监督视频目标分割算法，其精度与耗时最长的在线学习模型相当，而速度则与次优精度的最快模板匹配方法相似。其中，核心组件是使用全局上下文模块，通过整个视频有效地总结和传递信息。相较于之前只使用一个或少量帧来指导当前帧分割的方法，全局上下文模块使用所有过去的帧。此外，与之前的最佳空时记忆网络不同，全局上下文模块使用固定大小的特征表示，因此无论视频长度多长，其内存使用和计算成本都会大大降低。使用这种新模块，我们的模型可以在实时速度下达到标准基准的最高性能。