Heatmap regression has become the most prevalent choice for nowadays human
pose estimation methods. The ground-truth heatmaps are usually constructed via
covering all skeletal keypoints by 2D gaussian kernels. The standard deviations
of these kernels are fixed. However, for bottom-up methods, which need to
handle a large variance of human scales and labeling ambiguities, the current
practice seems unreasonable. To better cope with these problems, we propose the
scale-adaptive heatmap regression (SAHR) method, which can adaptively adjust
the standard deviation for each keypoint. In this way, SAHR is more tolerant of
various human scales and labeling ambiguities. However, SAHR may aggravate the
imbalance between fore-background samples, which potentially hurts the
improvement of SAHR. Thus, we further introduce the weight-adaptive heatmap
regression (WAHR) to help balance the fore-background samples. Extensive
experiments show that SAHR together with WAHR largely improves the accuracy of
bottom-up human pose estimation. As a result, we finally outperform the
state-of-the-art model by +1.5AP and achieve 72.0AP on COCO test-dev2017, which
is com-arable with the performances of most top-down methods. Source codes are
available at this https URL

本研究提出了基于尺寸自适应和权重自适应的热度图回归方法用于人体姿态估计，通过适应性地调整每个关键点的标准偏差以更好地面对人体尺度以及标注的不确定性。实验证明，该方法可以显著提高底部姿态估计的准确性，使其超越现有状态下最先进的顶部方法，并在 COCO 测试 - dev2017 上获得 72.0AP 的成绩。

重新思考基于热力图回归的自下而上的人体姿势估计

Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation

Crowd counting is to estimate the number of objects (e.g., people or
vehicles) in an image of unconstrained congested scenes. Designing a general
crowd counting algorithm applicable to a wide range of crowd images is
challenging, mainly due to the possibly large variation in object scales and
the presence of many isolated small clusters. Previous approaches based on
convolution operations with multi-branch architecture are effective for only
some narrow bands of scales and have not captured the long-range contextual
relationship due to isolated clustering. To address that, we propose SACANet, a
novel scale-adaptive long-range context-aware network for crowd counting.
SACANet consists of three major modules: the pyramid contextual module which
extracts long-range contextual information and enlarges the receptive field, a
scale-adaptive self-attention multi-branch module to attain high scale
sensitivity and detection accuracy of isolated clusters, and a hierarchical
fusion module to fuse multi-level self-attention features. With group
normalization, SACANet achieves better optimality in the training process. We
have conducted extensive experiments using the VisDrone2019 People dataset, the
VisDrone2019 Vehicle dataset, and some other challenging benchmarks. As
compared with the state-of-the-art methods, SACANet is shown to be effective,
especially for extremely crowded conditions with diverse scales and scattered
clusters, and achieves much lower MAE as compared with baselines.

本研究提出了一种名为 SACANet 的新型尺度自适应、具有长程上下文感知能力的网络，用于解决高度拥挤、尺度变化大且存在多个孤立集群的人群计数问题，并且在测试中表现出更高的准确性和更低的平均绝对误差。