We propose 'Deep spatial context' (DSCon) method, which serves for
investigation of the attention-based vision models using the concept of spatial
context. It was inspired by histopathologists, however, the method can be
applied to various domains. The DSCon allows for a quantitative measure of the
spatial context's role using three Spatial Context Measures: $SCM_{features}$,
$SCM_{targets}$, $SCM_{residuals}$ to distinguish whether the spatial context
is observable within the features of neighboring regions, their target values
(attention scores) or residuals, respectively. It is achieved by integrating
spatial regression into the pipeline. The DSCon helps to verify research
questions. The experiments reveal that spatial relationships are much bigger in
the case of the classification of tumor lesions than normal tissues. Moreover,
it turns out that the larger the size of the neighborhood taken into account
within spatial regression, the less valuable contextual information is.
Furthermore, it is observed that the spatial context measure is the largest
when considered within the feature space as opposed to the targets and
residuals.

这篇研究提出了 “深度空间背景”（DSCon）方法，它利用空间背景的概念来研究基于注意力的视觉模型，在组织病理学家的启发下，该方法可以应用于各种领域。DSCon 通过将空间回归集成到流程中来定量衡量空间背景在特征、目标值（注意力得分）或残差的邻近区域特征中是否可观察，以此帮助验证研究问题。实验证明，在肿瘤病变的分类中，空间关系要比正常组织更重要。此外，研究还观察到，在空间回归中考虑的邻域大小越大，上下文信息的价值就越小。此外，观察到在特征空间中考虑的空间背景测量值最大，而目标值和残差中的空间背景测量值较小。

深度空间上下文：当注意力模型遇见空间回归

Deep spatial context: when attention-based models meet spatial  regression

Mixture models are well-established learning approaches that, in computer
vision, have mostly been applied to inverse or ill-defined problems. However,
they are general-purpose divide-and-conquer techniques, splitting the input
space into relatively homogeneous subsets in a data-driven manner. Not only
ill-defined but also well-defined complex problems should benefit from them. To
this end, we devise a framework for spatial regression using mixture density
networks. We realize the framework for object detection and human pose
estimation. For both tasks, a mixture model yields higher accuracy and divides
the input space into interpretable modes. For object detection, mixture
components focus on object scale, with the distribution of components closely
following that of ground truth the object scale. This practically alleviates
the need for multi-scale testing, providing a superior speed-accuracy
trade-off. For human pose estimation, a mixture model divides the data based on
viewpoint and uncertainty -- namely, front and back views, with back view
imposing higher uncertainty. We conduct experiments on the MS COCO dataset and
do not face any mode collapse.

本文提出了使用混合密度网络进行空间回归的框架，在物体检测和人体姿态估计任务中，混合模型提高了准确性并使输入空间划分为可解释的模式。

混合密集回归用于物体检测和人体姿态估计

Mixture Dense Regression for Object Detection and Human Pose Estimation

Given a textual description of an image, phrase grounding localizes objects
in the image referred by query phrases in the description. State-of-the-art
methods address the problem by ranking a set of proposals based on the
relevance to each query, which are limited by the performance of independent
proposal generation systems and ignore useful cues from context in the
description. In this paper, we adopt a spatial regression method to break the
performance limit, and introduce reinforcement learning techniques to further
leverage semantic context information. We propose a novel Query-guided
Regression network with Context policy (QRC Net) which jointly learns a
Proposal Generation Network (PGN), a Query-guided Regression Network (QRN) and
a Context Policy Network (CPN). Experiments show QRC Net provides a significant
improvement in accuracy on two popular datasets: Flickr30K Entities and Referit
Game, with 14.25% and 17.14% increase over the state-of-the-arts respectively.

本文提出一种新的 Query-Regured Regression 网络（QRC Net）方法，利用空间回归方法打破最优提案性能限制，同时引入强化学习技术并进一步利用语义上下文信息提高了准确性，显著提高了两个流行数据集（Flickr30K Entities 和 Referit Game）上的表现。