This paper proposes a new active learning method for semantic segmentation.
The core of our method lies in a new annotation query design. It samples
informative local image regions (e.g., superpixels), and for each of such
regions, asks an oracle for a multi-hot vector indicating all classes existing
in the region. This multi-class labeling strategy is substantially more
efficient than existing ones like segmentation, polygon, and even dominant
class labeling in terms of annotation time per click. However, it introduces
the class ambiguity issue in training since it assigns partial labels (i.e., a
set of candidate classes) to individual pixels. We thus propose a new algorithm
for learning semantic segmentation while disambiguating the partial labels in
two stages. In the first stage, it trains a segmentation model directly with
the partial labels through two new loss functions motivated by partial label
learning and multiple instance learning. In the second stage, it disambiguates
the partial labels by generating pixel-wise pseudo labels, which are used for
supervised learning of the model. Equipped with a new acquisition function
dedicated to the multi-class labeling, our method outperformed previous work on
Cityscapes and PASCAL VOC 2012 while spending less annotation cost.

该论文提出了一种新的语义分割主动学习方法，通过新的注释查询设计获取信息丰富的局部图像区域，使用多类别标注策略进行训练，解决了类别不确定性问题，最终在 Cityscapes 和 PASCAL VOC 2012 数据集上取得了优越的性能并降低了注释成本。

多分类标签查询的语义分割主动学习

Active Learning for Semantic Segmentation with Multi-class Label Query

Crowdsourcing has become an effective and popular tool for human-powered
computation to label large datasets. Since the workers can be unreliable, it is
common in crowdsourcing to assign multiple workers to one task, and to
aggregate the labels in order to obtain results of high quality. In this paper,
we provide finite-sample exponential bounds on the error rate (in probability
and in expectation) of general aggregation rules under the Dawid-Skene
crowdsourcing model. The bounds are derived for multi-class labeling, and can
be used to analyze many aggregation methods, including majority voting,
weighted majority voting and the oracle Maximum A Posteriori (MAP) rule. We
show that the oracle MAP rule approximately optimizes our upper bound on the
mean error rate of weighted majority voting in certain setting. We propose an
iterative weighted majority voting (IWMV) method that optimizes the error rate
bound and approximates the oracle MAP rule. Its one step version has a provable
theoretical guarantee on the error rate. The IWMV method is intuitive and
computationally simple. Experimental results on simulated and real data show
that IWMV performs at least on par with the state-of-the-art methods, and it
has a much lower computational cost (around one hundred times faster) than the
state-of-the-art methods.

本文针对人力众包任务标注中工作者不可靠的情况，提出一种基于权重投票的错误率上界优化方法，并给出了多类标注错误率（包括期望和概率）的有限样本指数界。实验结果表明该方法不仅在模拟数据上表现良好，而且在实际数据上与现有方法一致，但计算成本要小大约 100 倍。

众包中误差率界限与迭代加权多数表决

Error Rate Bounds and Iterative Weighted Majority Voting for  Crowdsourcing

Crowdsourcing is a popular paradigm for effectively collecting labels at low
cost. The Dawid-Skene estimator has been widely used for inferring the true
labels from the noisy labels provided by non-expert crowdsourcing workers.
However, since the estimator maximizes a non-convex log-likelihood function, it
is hard to theoretically justify its performance. In this paper, we propose a
two-stage efficient algorithm for multi-class crowd labeling problems. The
first stage uses the spectral method to obtain an initial estimate of
parameters. Then the second stage refines the estimation by optimizing the
objective function of the Dawid-Skene estimator via the EM algorithm. We show
that our algorithm achieves the optimal convergence rate up to a logarithmic
factor. We conduct extensive experiments on synthetic and real datasets.
Experimental results demonstrate that the proposed algorithm is comparable to
the most accurate empirical approach, while outperforming several other
recently proposed methods.

本研究提出了一种用于多类众包标注问题的两阶段高效算法，第一阶段使用谱方法获取参数的初始估计，然后第二阶段通过 EM 算法优化 Dawid-Skene 估计器的目标函数来进一步细化估计，实验结果表明该算法具有最优收敛速率。