The augmentation parameters matter to few-shot semantic segmentation since
they directly affect the training outcome by feeding the networks with varying
perturbated samples. However, searching optimal augmentation parameters for
few-shot segmentation models without annotations is a challenge that current
methods fail to address. In this paper, we first propose a framework to
determine the ``optimal'' parameters without human annotations by solving a
distribution-matching problem between the intra-instance and intra-class
similarity distribution, with the intra-instance similarity describing the
similarity between the original sample of a particular anatomy and its
augmented ones and the intra-class similarity representing the similarity
between the selected sample and the others in the same class. Extensive
experiments demonstrate the superiority of our optimized augmentation in
boosting few-shot segmentation models. We greatly improve the top competing
method by 1.27\% and 1.11\% on Abd-MRI and Abd-CT datasets, respectively, and
even achieve a significant improvement for SSL-ALP on the left kidney by 3.39\%
on the Abd-CT dataset.

本研究提出了一种在没有人工注释的情况下通过解决部位内相似度和类内相似度之间分配匹配问题来确定最佳数据增强参数的优化框架，进而在医学图像分割中证明了其对于提升 few-shot 模型性能的有效性。

无监督增强优化用于小样本医学图像分割

Unsupervised augmentation optimization for few-shot medical image  segmentation

Sequential transfer optimization (STO), which aims to improve optimization
performance by exploiting knowledge captured from previously-solved
optimization tasks stored in a database, has been gaining increasing research
attention in recent years. However, despite significant advancements in
algorithm design, the test problems in STO are not well designed. Oftentimes,
they are either randomly assembled by other benchmark functions that have
identical optima or are generated from practical problems that exhibit limited
variations. The relationships between the optimal solutions of source and
target tasks in these problems are manually configured and thus monotonous,
limiting their ability to represent the diverse relationships of real-world
problems. Consequently, the promising results achieved by many algorithms on
these problems are highly biased and difficult to be generalized to other
problems. In light of this, we first introduce a few rudimentary concepts for
characterizing STO problems (STOPs) and present an important problem feature
overlooked in previous studies, namely similarity distribution, which
quantitatively delineates the relationship between the optima of source and
target tasks. Then, we propose general design guidelines and a problem
generator with superior extendibility. Specifically, the similarity
distribution of a problem can be systematically customized by modifying a
parameterized density function, enabling a broad spectrum of representation for
the diverse similarity relationships of real-world problems. Lastly, a
benchmark suite with 12 individual STOPs is developed using the proposed
generator, which can serve as an arena for comparing different STO algorithms.
The source code of the benchmark suite is available at
this https URL

本文针对现有的测试问题不能很好地设计 STO 问题，提出了表示 STO 问题的简单概念，介绍了被之前的研究所忽视的重要问题特征，提出了一些设计准则和一种具有超强可扩展性的问题生成器以及 12 个 STOPs 的基准套件，可以用来比较不同的 STO 算法。

面向顺序转移优化的可伸缩测试问题生成器

A Scalable Test Problem Generator for Sequential Transfer Optimization

Online hashing has attracted extensive research attention when facing
streaming data. Most online hashing methods, learning binary codes based on
pairwise similarities of training instances, fail to capture the semantic
relationship, and suffer from a poor generalization in large-scale applications
due to large variations. In this paper, we propose to model the similarity
distributions between the input data and the hashing codes, upon which a novel
supervised online hashing method, dubbed as Similarity Distribution based
Online Hashing (SDOH), is proposed, to keep the intrinsic semantic relationship
in the produced Hamming space. Specifically, we first transform the discrete
similarity matrix into a probability matrix via a Gaussian-based normalization
to address the extremely imbalanced distribution issue. And then, we introduce
a scaling Student t-distribution to solve the challenging initialization
problem, and efficiently bridge the gap between the known and unknown
distributions. Lastly, we align the two distributions via minimizing the
Kullback-Leibler divergence (KL-diverence) with stochastic gradient descent
(SGD), by which an intuitive similarity constraint is imposed to update hashing
model on the new streaming data with a powerful generalizing ability to the
past data. Extensive experiments on three widely-used benchmarks validate the
superiority of the proposed SDOH over the state-of-the-art methods in the
online retrieval task.

本研究提出一种基于相似度分布的在线哈希方法 (SDOH)，采用高斯归一化处理来解决极不平衡分布问题并通过最小化 KL 散度来对齐相似度分布，最后在三个广泛使用的基准测试中验证了该方法的优越性。