Despite the recent advances in multi-task learning of dense prediction
problems, most methods rely on expensive labelled datasets. In this paper, we
present a label efficient approach and look at jointly learning of multiple
dense prediction tasks on partially annotated data (i.e. not all the task
labels are available for each image), which we call multi-task
partially-supervised learning. We propose a multi-task training procedure that
successfully leverages task relations to supervise its multi-task learning when
data is partially annotated. In particular, we learn to map each task pair to a
joint pairwise task-space which enables sharing information between them in a
computationally efficient way through another network conditioned on task
pairs, and avoids learning trivial cross-task relations by retaining high-level
information about the input image. We rigorously demonstrate that our proposed
method effectively exploits the images with unlabelled tasks and outperforms
existing semi-supervised learning approaches and related methods on three
standard benchmarks.

本研究提出了一种标签高效的方法，针对部分注释的数据（即每个图像不都有所有任务标签），在多个密集预测任务上联合学习，通过保留输入图像的高级信息，并成功利用任务关系对多任务学习进行监督学习的多任务训练程序。我们严格证明了我们的方法可以有效地利用具有未标记任务的图像，并且在三个标准基准测试中优于现有的半监督学习方法和相关方法。

从部分注释的数据中学习多个密集预测任务

Learning Multiple Dense Prediction Tasks from Partially Annotated Data

Inspired by the hierarchical hidden Markov models (HHMM), we present the
hierarchical semi-Markov conditional random field (HSCRF), a generalisation of
embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov
processes. It is parameterised in a discriminative framework and has polynomial
time algorithms for learning and inference. Importantly, we consider
partiallysupervised learning and propose algorithms for generalised
partially-supervised learning and constrained inference. We demonstrate the
HSCRF in two applications: (i) recognising human activities of daily living
(ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show
that the HSCRF is capable of learning rich hierarchical models with reasonable
accuracy in both fully and partially observed data cases.

本文提出了一种基于部分监督学习的、以区分性参数化的、具有多项式时间算法的分层半马尔可夫条件随机场模型，可以用于人类活动检测和名词短语分块等应用中进行复杂的分层嵌套马尔可夫过程建模，并在部分监督和有监督的数据中展示其高精度。