Multi-scenario learning (MSL) enables a service provider to cater for users'
fine-grained demands by separating services for different user sectors, e.g.,
by user's geographical region. Under each scenario there is a need to optimize
multiple task-specific targets e.g., click through rate and conversion rate,
known as multi-task learning (MTL). Recent solutions for MSL and MTL are mostly
based on the multi-gate mixture-of-experts (MMoE) architecture. MMoE structure
is typically static and its design requires domain-specific knowledge, making
it less effective in handling both MSL and MTL. In this paper, we propose a
novel Automatic Expert Selection framework for Multi-scenario and Multi-task
search, named AESM^{2}. AESM^{2} integrates both MSL and MTL into a unified
framework with an automatic structure learning. Specifically, AESM^{2} stacks
multi-task layers over multi-scenario layers. This hierarchical design enables
us to flexibly establish intrinsic connections between different scenarios, and
at the same time also supports high-level feature extraction for different
tasks. At each multi-scenario/multi-task layer, a novel expert selection
algorithm is proposed to automatically identify scenario-/task-specific and
shared experts for each input. Experiments over two real-world large-scale
datasets demonstrate the effectiveness of AESM^{2} over a battery of strong
baselines. Online A/B test also shows substantial performance gain on multiple
metrics. Currently, AESM^{2} has been deployed online for serving major
traffic.

该论文提出了一种名为 AESM^{2} 的新型自动专家选择框架，该框架将多场景学习（MSL）和多任务学习（MTL）整合到一个统一的框架中，具有自动结构学习的特点，并采用分层设计和专家选择算法，实验表明在真实世界的大规模数据集上表现良好且有助于提高多项指标的在线 A/B 测试。

多场景多任务搜索自动专家选择

Automatic Expert Selection for Multi-Scenario and Multi-Task Search

Transformers have demonstrated great potential in computer vision tasks. To
avoid dense computations of self-attentions in high-resolution visual data,
some recent Transformer models adopt a hierarchical design, where
self-attentions are only computed within local windows. This design
significantly improves the efficiency but lacks global feature reasoning in
early stages. In this work, we design a multi-path structure of the
Transformer, which enables local-to-global reasoning at multiple granularities
in each stage. The proposed framework is computationally efficient and highly
effective. With a marginal increasement in computational overhead, our model
achieves notable improvements in both image classification and semantic
segmentation. Code is available at this https URL

本研究提出了多通路结构的 Transformer 模型，实现局部到全局的多粒度特征推理，相较于现有的分层设计模型，在增加了极小的计算量的同时，在图像分类和语义分割任务上取得了显著的提高。

局部到全局的视觉 Transformer 自注意力

Local-to-Global Self-Attention in Vision Transformers

We explore the problem of real-time stereo matching on high-res imagery. Many
state-of-the-art (SOTA) methods struggle to process high-res imagery because of
memory constraints or speed limitations. To address this issue, we propose an
end-to-end framework that searches for correspondences incrementally over a
coarse-to-fine hierarchy. Because high-res stereo datasets are relatively rare,
we introduce a dataset with high-res stereo pairs for both training and
evaluation. Our approach achieved SOTA performance on Middlebury-v3 and
KITTI-15 while running significantly faster than its competitors. The
hierarchical design also naturally allows for anytime on-demand reports of
disparity by capping intermediate coarse results, allowing us to accurately
predict disparity for near-range structures with low latency (30ms). We
demonstrate that the performance-vs-speed trade-off afforded by on-demand
hierarchies may address sensing needs for time-critical applications such as
autonomous driving.

我们提出了一种端到端的框架，通过逐层搜索相应关系来解决高分辨率图像上的实时立体匹配问题，利用所提出的数据集进行训练和评估并在速度上超过竞争对手 ，能够在低延迟（30 毫秒）内准确预测近距离结构的视差，所提出的层次结构设计可以灵活地平衡性能与速度，用于自动驾驶等时间关键的应用。