In this work, we present a novel approach to calibrate segmentation networks
that considers the inherent challenges posed by different categories and object
regions. In particular, we present a formulation that integrates class and
region-wise constraints into the learning objective, with multiple penalty
weights to account for class and region differences. Finding the optimal
penalty weights manually, however, might be unfeasible, and potentially hinder
the optimization process. To overcome this limitation, we propose an approach
based on Class and Region-Adaptive constraints (CRaC), which allows to learn
the class and region-wise penalty weights during training. CRaC is based on a
general Augmented Lagrangian method, a well-established technique in
constrained optimization. Experimental results on two popular segmentation
benchmarks, and two well-known segmentation networks, demonstrate the
superiority of CRaC compared to existing approaches. The code is available at:
this https URL

本文提出了一种新的方法来校准分割网络，考虑了不同类别和目标区域所带来的困难。具体来说，我们提出了一种将类别和区域约束融入学习目标的公式，利用多个惩罚权重来解决类别和区域差异的问题。然而，手动找到最佳的惩罚权重可能是不可行的，会阻碍优化过程。为了克服这个限制，我们提出了一种基于类别和区域自适应约束（CRaC）的方法，可以在训练过程中学习类别和区域的惩罚权重。CRaC 基于广义增广拉格朗日方法，这是一种在约束优化中广泛应用的技术。在两个流行的分割基准和两个知名的分割网络上进行的实验证明了 CRaC 相比现有方法的优势。代码可以在此链接中找到：https:// 这个网址。

网络校准的类别和区域自适应约束

Class and Region-Adaptive Constraints for Network Calibration

Optimisation algorithms designed to work on quantum computers or other
specialised hardware have been of research interest in recent years. Many of
these solver can only optimise problems that are in binary and quadratic form.
Quadratic Unconstrained Binary Optimisation (QUBO) is therefore a common
formulation used by these solvers.
There are many combinatorial optimisation problems that are naturally
represented as permutations e.g., travelling salesman problem. Encoding
permutation problems using binary variables however presents some challenges.
Many QUBO solvers are single flip solvers, it is therefore possible to generate
solutions that cannot be decoded to a valid permutation. To create bias towards
generating feasible solutions, we use penalty weights. The process of setting
static penalty weights for various types of problems is not trivial. This is
because values that are too small will lead to infeasible solutions being
returned by the solver while values that are too large may lead to slower
convergence. In this study, we explore some methods of setting penalty weights
within the context of QUBO formulations. We propose new static methods of
calculating penalty weights which lead to more promising results than existing
methods.

本文研究了使用量子计算机或其他专用硬件的优化算法，介绍了 QUBO 形式的优化问题，并探索了在 QUBO 公式下设置罚项权重的新静态方法，以解决旅行商问题等排列问题的优化难题。