Human keypoint detection from a single image is very challenging due to
occlusion, blur, illumination and scale variance. In this paper, we address
this problem from three aspects by devising an efficient network structure,
proposing three effective training strategies, and exploiting four useful
postprocessing techniques. First, we find that context information plays an
important role in reasoning human body configuration and invisible keypoints.
Inspired by this, we propose a cascaded context mixer (CCM), which efficiently
integrates spatial and channel context information and progressively refines
them. Then, to maximize CCM's representation capability, we develop a
hard-negative person detection mining strategy and a joint-training strategy by
exploiting abundant unlabeled data. It enables CCM to learn discriminative
features from massive diverse poses. Third, we present several sub-pixel
refinement techniques for postprocessing keypoint predictions to improve
detection accuracy. Extensive experiments on the MS COCO keypoint detection
benchmark demonstrate the superiority of the proposed method over
representative state-of-the-art (SOTA) methods. Our single model achieves
comparable performance with the winner of the 2018 COCO Keypoint Detection
Challenge. The final ensemble model sets a new SOTA on this benchmark.

本研究提出了一种用于人体关键点检测的有效网络结构、三种有效的训练策略和四种有用的后处理技术，通过积极利用丰富的非标记数据和上下文信息来提高检测准确性，从各个方面解决了由于遮挡、模糊、光照和尺度差异等因素所导致的挑战性问题，并在 MS COCO 关键点检测基准测试上取得了领先于其他参考方法的结果。

高性能人体关键点检测技术

Towards High Performance Human Keypoint Detection

Human keypoint detection from a single image is very challenging due to
occlusion, blur, illumination and scale variance of person instances. In this
paper, we find that context information plays an important role in addressing
these issues, and propose a novel method named progressive context refinement
(PCR) for human keypoint detection. First, we devise a simple but effective
context-aware module (CAM) that can efficiently integrate spatial and channel
context information to aid feature learning for locating hard keypoints. Then,
we construct the PCR model by stacking several CAMs sequentially with shortcuts
and employ multi-task learning to progressively refine the context information
and predictions. Besides, to maximize PCR's potential for the aforementioned
hard case inference, we propose a hard-negative person detection mining
strategy together with a joint-training strategy by exploiting the unlabeled
coco dataset and external dataset. Extensive experiments on the COCO keypoint
detection benchmark demonstrate the superiority of PCR over representative
state-of-the-art (SOTA) methods. Our single model achieves comparable
performance with the winner of the 2018 COCO Keypoint Detection Challenge. The
final ensemble model sets a new SOTA on this benchmark.

本文提出了一种称为 PCR 的新方法，通过多任务学习和利用上下文信息来提高人体关键点检测的性能，并通过利用未标记的 coco 数据集和外部数据集来实现难例推理的最大化潜能。实验结果表明，PCR 模型优于代表最先进的模型，并在 COCO 关键点检测基准测试上取得了最新的最佳表现。

渐进式上下文精炼的人体关键点检测

Human Keypoint Detection by Progressive Context Refinement

Significant progress has been achieved in Computer Vision by leveraging
large-scale image datasets. However, large-scale datasets for complex Computer
Vision tasks beyond classification are still limited. This paper proposed a
large-scale dataset named AIC (AI Challenger) with three sub-datasets, human
keypoint detection (HKD), large-scale attribute dataset (LAD) and image Chinese
captioning (ICC). In this dataset, we annotate class labels (LAD), keypoint
coordinate (HKD), bounding box (HKD and LAD), attribute (LAD) and caption
(ICC). These rich annotations bridge the semantic gap between low-level images
and high-level concepts. The proposed dataset is an effective benchmark to
evaluate and improve different computational methods. In addition, for related
tasks, others can also use our dataset as a new resource to pre-train their
models.

提出了一个名为 AIC 的大规模数据集，其中包括人类关键点检测、大规模属性数据集和图像中文字幕，用于评估和改进计算方法。