This study performs an attribute-level analysis of the global and local path
preferences of network travelers. To this end, a reward decomposition approach
is proposed and integrated into a link-based recursive (Markovian) path choice
model. The approach decomposes the instantaneous reward function associated
with each state-action pair into the global utility, a function of attributes
globally perceived from anywhere in the network, and the local utility, a
function of attributes that are only locally perceived from the current state.
Only the global utility then enters the value function of each state,
representing the future expected utility toward the destination. This
global-local path choice model with decomposed reward functions allows us to
analyze to what extent and which attributes affect the global and local path
choices of agents. Moreover, unlike most adaptive path choice models, the
proposed model can be estimated based on revealed path observations (without
the information of plans) and as efficiently as deterministic recursive path
choice models. The model was applied to the real pedestrian path choice
observations in an urban street network where the green view index was
extracted as a visual street quality from Google Street View images. The result
revealed that pedestrians locally perceive and react to the visual street
quality, rather than they have the pre-trip global perception on it.
Furthermore, the simulation results using the estimated models suggested the
importance of location selection of interventions when policy-related
attributes are only locally perceived by travelers.

本文研究行人的路径选择行为，提出了一种基于奖励分解的全局和局部路径选择模型，并将其应用于城市街道网络中。研究结果表明，行人在路径选择时更注重局部特征，例如道路视觉质量，而非全局特征。此外，本文的模拟结果认为，在政策相关属性只局部被旅行者感知时，位置选择干预措施非常重要。

全球路径偏好和本地响应：一种奖励分解方法，用于网络路径选择分析中的局部属性感知

Global path preference and local response: A reward decomposition  approach for network path choice analysis in the presence of locally  perceived attributes

Recently, self-attention mechanisms have shown impressive performance in
various NLP and CV tasks, which can help capture sequential characteristics and
derive global information. In this work, we explore how to extend
self-attention modules to better learn subtle feature embeddings for
recognizing fine-grained objects, e.g., different bird species or person
identities. To this end, we propose a dual cross-attention learning (DCAL)
algorithm to coordinate with self-attention learning. First, we propose
global-local cross-attention (GLCA) to enhance the interactions between global
images and local high-response regions, which can help reinforce the
spatial-wise discriminative clues for recognition. Second, we propose pair-wise
cross-attention (PWCA) to establish the interactions between image pairs. PWCA
can regularize the attention learning of an image by treating another image as
distractor and will be removed during inference. We observe that DCAL can
reduce misleading attentions and diffuse the attention response to discover
more complementary parts for recognition. We conduct extensive evaluations on
fine-grained visual categorization and object re-identification. Experiments
demonstrate that DCAL performs on par with state-of-the-art methods and
consistently improves multiple self-attention baselines, e.g., surpassing
DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively.

本文介绍了一种使用双重交叉 - 关注学习算法改进细粒度识别的方法，并通过全局 - 本地交叉关注和成对交叉关注等策略来提高自我关注机制的性能，从而使得注意力响应更加合理，能够发现更多互补的特征部位以提高识别精度。