Spatio-temporal point process (STPP) is a stochastic collection of events
accompanied with time and space. Due to computational complexities, existing
solutions for STPPs compromise with conditional independence between time and
space, which consider the temporal and spatial distributions separately. The
failure to model the joint distribution leads to limited capacities in
characterizing the spatio-temporal entangled interactions given past events. In
this work, we propose a novel parameterization framework for STPPs, which
leverages diffusion models to learn complex spatio-temporal joint
distributions. We decompose the learning of the target joint distribution into
multiple steps, where each step can be faithfully described by a Gaussian
distribution. To enhance the learning of each step, an elaborated
spatio-temporal co-attention module is proposed to capture the interdependence
between the event time and space adaptively. For the first time, we break the
restrictions on spatio-temporal dependencies in existing solutions, and enable
a flexible and accurate modeling paradigm for STPPs. Extensive experiments from
a wide range of fields, such as epidemiology, seismology, crime, and urban
mobility, demonstrate that our framework outperforms the state-of-the-art
baselines remarkably, with an average improvement of over 50%. Further in-depth
analyses validate its ability to capture spatio-temporal interactions, which
can learn adaptively for different scenarios. The datasets and source code are
available online:
this https URL

该研究提出了一种新的基于扩散模型的参数化框架，用于学习复杂的时空联合分布，它打破了现有解决方案对时空依赖关系的限制，并实现了灵活准确的预测。

时空扩散点过程

Spatio-temporal Diffusion Point Processes

Co-saliency detection aims to detect common salient objects from a group of
relevant images. Some attempts have been made with the Fully Convolutional
Network (FCN) framework and achieve satisfactory detection results. However,
due to stacking convolution layers and pooling operation, the boundary details
tend to be lost. In addition, existing models often utilize the extracted
features without discrimination, leading to redundancy in representation since
actually not all features are helpful to the final prediction and some even
bring distraction. In this paper, we propose a co-attention module embedded FCN
framework, called as Co-Attention FCN (CA-FCN). Specifically, the co-attention
module is plugged into the high-level convolution layers of FCN, which can
assign larger attention weights on the common salient objects and smaller ones
on the background and uncommon distractors to boost final detection
performance. Extensive experiments on three popular co-saliency benchmark
datasets demonstrate the superiority of the proposed CA-FCN, which outperforms
state-of-the-arts in most cases. Besides, the effectiveness of our new
co-attention module is also validated with ablation studies.

本文提出了嵌入了共同注意力模块的全卷积网络框架，称为共同注意力 FCN (CA-FCN)，旨在检测一组相关图像中的共同显著对象。实验表明，CA-FCN 在大多数情况下优于现有算法，同时通过消融研究证明了新共同注意力模块的有效性。

基于 Co-Attention 全卷积网络的共同显著性检测

Co-Saliency Detection with Co-Attention Fully Convolutional Network

We propose a new approach to determine correspondences between image pairs in
the wild under large changes in illumination, viewpoint, context, and material.
While other approaches find correspondences between pairs of images by treating
the images independently, we instead condition on both images to implicitly
take account of the differences between them. To achieve this, we introduce (i)
a spatial attention mechanism (a co-attention module, CoAM) for conditioning
the learned features on both images, and (ii) a distinctiveness score used to
choose the best matches at test time. CoAM can be added to standard
architectures and trained using self-supervision or supervised data, and
achieves a significant performance improvement under hard conditions, e.g.
large viewpoint changes. We demonstrate that models using CoAM achieve state of
the art or competitive results on a wide range of tasks: local matching, camera
localization, 3D reconstruction, and image stylization.

提出了一种新的方法来确定自然环境下图像对之间的对应关系，在大的光照变化、视角变化、语境和材料下进行。该方法引入了一种空间注意力机制和一个独特性得分，能够改善对这些因素变化下的对应关系的检测，并在许多任务中实现了最先进或具有竞争力的结果。