Most of the top-down pose estimation models assume that there exists only one
person in a bounding box. However, the assumption is not always correct. In
this technical report, we introduce two ideas, instance cue and recurrent
refinement, to an existing pose estimator so that the model is able to handle
detection boxes with multiple persons properly. When we evaluated our model on
the COCO17 keypoints dataset, it showed non-negligible improvement compared to
its baseline model. Our model achieved 76.2 mAP as a single model and 77.3 mAP
as an ensemble on the test-dev set without additional training data. After
additional post-processing with a separate refinement network, our final
predictions achieved 77.8 mAP on the COCO test-dev set.

本研究通过引入两个新的概念 - instance cue 和 recurrent refinement 到已有的姿态估计模型中，以处理带有多个人的检测框，实现了更准确的姿态估计，并在 COCO17 关键点数据集上取得了较高的 mAP 得分。

HintPose

This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
predict the binary label of each pixel on the input image, we train the neural
network to learn the degradations in document images and produce the uniform
images of the degraded input images, which allows the network to refine the
output iteratively. Two different iterative methods have been studied in this
paper: recurrent refinement (RR) which uses the same trained neural network in
each iteration for document enhancement and stacked refinement (SR) which uses
a stack of different neural networks for iterative output refinement. Given the
learned uniform and enhanced image, the binarization map can be easy to obtain
by a global or local threshold. The experimental results on several public
benchmark data sets show that our proposed methods provide a new clean version
of the degraded image which is suitable for visualization and promising results
of binarization using the global Otsu's threshold based on the enhanced images
learned iteratively by the neural network.

本文提出了一种新的迭代深度学习框架，并将其应用于文档增强和二值化。通过学习文档图像中的退化过程，该网络能够逐步细化输出，实现文档增强。通过在全局或本地阈值下使用增强的图像，可以获得二值化图。实验结果表明，所提出的方法适用于可视化和基于 Otsu 阈值的二值化处理。

DeepOTSU：使用迭代深度学习进行文档增强和二值化

DeepOtsu: Document Enhancement and Binarization using Iterative Deep  Learning

Crowd counting from unconstrained scene images is a crucial task in many
real-world applications like urban surveillance and management, but it is
greatly challenged by the camera's perspective that causes huge appearance
variations in people's scales and rotations. Conventional methods address such
challenges by resorting to fixed multi-scale architectures that are often
unable to cover the largely varied scales while ignoring the rotation
variations. In this paper, we propose a unified neural network framework, named
Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues
in a learnable spatial transform module with a region-wise refinement process.
Specifically, our framework incorporates a Recurrent Spatial-Aware Refinement
(RSAR) module iteratively conducting two components: i) a Spatial Transformer
Network that dynamically locates an attentional region from the crowd density
map and transforms it to the suitable scale and rotation for optimal crowd
estimation; ii) a Local Refinement Network that refines the density map of the
attended region with residual learning. Extensive experiments on four
challenging benchmarks show the effectiveness of our approach. Specifically,
comparing with the existing best-performing methods, we achieve an improvement
of 12% on the largest dataset WorldExpo'10 and 22.8% on the most challenging
dataset UCF_CC_50.

本文提出了一种利用神经网络及空间变换模型解决人流量不规则变化问题的方法，在 4 个数据集上实验得到了 12%-22.8% 的改进。