论文标题
援助：通过信息降低增强，推动人姿势估计的性能边界
AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation
论文作者
论文摘要
外观提示和约束提示对于人姿势估计至关重要。但是，大多数现有的作品都倾向于使前者过度拟合并忽略后者。在本文中，我们提出通过信息删除（AID）来验证和应对这一困境的增强。我们仅在有效利用其潜力的先决条件下仅提供援助，我们提出了定制的培训时间表，这些计划是通过从信息供应的角度分析培训过程中损失和绩效模式而设计的。在实验中，AID作为模型无形方法，促进了具有不同输入尺寸，框架，骨架，训练和测试集的自下而上和自上而下的范式中的各种最新方法。在流行的可可人体姿势估计测试集上，辅助始终在自上而下的范式中提高不同配置的性能，而自下而上的范式中的性能高达1.5 ap。在更具挑战性的人群数据集中，改进超过1.5 AP。随着援助成功地通过相当大的余量推动了人类姿势估计问题的性能边界，并设定了新的最先进，我们希望AID成为训练人姿势估计器的常规配置。源代码将公开用于进一步研究。
Both appearance cue and constraint cue are vital for human pose estimation. However, there is a tendency in most existing works to overfitting the former and overlook the latter. In this paper, we propose Augmentation by Information Dropping (AID) to verify and tackle this dilemma. Alone with AID as a prerequisite for effectively exploiting its potential, we propose customized training schedules, which are designed by analyzing the pattern of loss and performance in training process from the perspective of information supplying. In experiments, as a model-agnostic approach, AID promotes various state-of-the-art methods in both bottom-up and top-down paradigms with different input sizes, frameworks, backbones, training and testing sets. On popular COCO human pose estimation test set, AID consistently boosts the performance of different configurations by around 0.6 AP in top-down paradigm and up to 1.5 AP in bottom-up paradigm. On more challenging CrowdPose dataset, the improvement is more than 1.5 AP. As AID successfully pushes the performance boundary of human pose estimation problem by considerable margin and sets a new state-of-the-art, we hope AID to be a regular configuration for training human pose estimators. The source code will be publicly available for further research.
