Despite the rapid advancement in the field of image recognition, the
processing of high-resolution imagery remains a computational challenge.
However, this processing is pivotal for extracting detailed object insights in
areas ranging from autonomous vehicle navigation to medical imaging analyses.
Our study introduces a framework aimed at mitigating these challenges by
leveraging memory efficient patch based processing for high resolution images.
It incorporates a global context representation alongside local patch
information, enabling a comprehensive understanding of the image content. In
contrast to traditional training methods which are limited by memory
constraints, our method enables training of ultra high resolution images. We
demonstrate the effectiveness of our method through superior performance on 7
different benchmarks across classification, object detection, and segmentation.
Notably, the proposed method achieves strong performance even on
resource-constrained devices like Jetson Nano. Our code is available at
this https URL

应对高分辨率图像处理中的计算挑战，提出了一种利用内存高效的基于块处理的框架，通过全局上下文表示和局部块信息实现对图像内容的全面理解，并在不受内存限制的情况下对超高分辨率图像进行训练，在分类、目标检测和分割等七个基准测试中取得了卓越性能，甚至在像 Jetson Nano 这样的资源受限设备上也能表现出色。

视觉系统的资源高效感知

Resource Efficient Perception for Vision Systems

The unit selection problem aims to find objects, called units, that optimize
a causal objective function which describes the objects' behavior in a causal
context (e.g., selecting customers who are about to churn but would most likely
change their mind if encouraged). While early studies focused mainly on
bounding a specific class of counterfactual objective functions using data,
more recent work allows one to find optimal units exactly by reducing the
causal objective to a classical objective on a meta-model, and then applying a
variant of the classical Variable Elimination (VE) algorithm to the meta-model
-- assuming a fully specified causal model is available. In practice, however,
finding optimal units using this approach can be very expensive because the
used VE algorithm must be exponential in the constrained treewidth of the
meta-model, which is larger and denser than the original model. We address this
computational challenge by introducing a new approach for unit selection that
is not necessarily limited by the constrained treewidth. This is done through
compiling the meta-model into a special class of tractable arithmetic circuits
that allows the computation of optimal units in time linear in the circuit
size. We finally present empirical results on random causal models that show
order-of-magnitude speedups based on the proposed method for solving unit
selection.

通过将元模型编译成一种特殊的可处理算术电路，我们提出了一种新的单元选择方法，不受约束树宽的限制，并展示了基于该方法解决单元选择问题的数量级速度提升。

使用可解算算术电路的因果单元选择

Causal Unit Selection using Tractable Arithmetic Circuits

This paper presents the computational challenge on topological deep learning
that was hosted within the ICML 2023 Workshop on Topology and Geometry in
Machine Learning. The competition asked participants to provide open-source
implementations of topological neural networks from the literature by
contributing to the python packages TopoNetX (data processing) and TopoModelX
(deep learning). The challenge attracted twenty-eight qualifying submissions in
its two-month duration. This paper describes the design of the challenge and
summarizes its main findings.

这篇论文概述了在 ICML 2023《拓扑与几何在机器学习中的应用》研讨会中举办的拓扑深度学习的计算挑战，并描述了挑战的设计和主要发现。