Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while
exploiting their mutual relationships. By using shared resources to
simultaneously calculate multiple outputs, this learning paradigm has the
potential to have lower memory requirements and inference times compared to the
traditional approach of using separate methods for each task. Previous work in
MTL has mainly focused on fully-supervised methods, as task relationships can
not only be leveraged to lower the level of data-dependency of those methods
but they can also improve performance. However, MTL introduces a set of
challenges due to a complex optimisation scheme and a higher labeling
requirement. This review focuses on how MTL could be utilised under different
partial supervision settings to address these challenges. First, this review
analyses how MTL traditionally uses different parameter sharing techniques to
transfer knowledge in between tasks. Second, it presents the different
challenges arising from such a multi-objective optimisation scheme. Third, it
introduces how task groupings can be achieved by analysing task relationships.
Fourth, it focuses on how partially supervised methods applied to MTL can
tackle the aforementioned challenges. Lastly, this review presents the
available datasets, tools and benchmarking results of such methods.

多任务学习通过使用共享资源同时计算多个输出，具有比传统方法更低的内存需求和推理时间，本综述主要研究多任务学习如何在不同的部分监督设置下应用以解决相关挑战。

多任务学习与部分监督的结合：计算机视觉综述

When Multi-Task Learning Meets Partial Supervision: A Computer Vision  Review

Developing an AI-assisted gland segmentation method from histology images is
critical for automatic cancer diagnosis and prognosis; however, the high cost
of pixel-level annotations hinders its applications to broader diseases.
Existing weakly-supervised semantic segmentation methods in computer vision
achieve degenerative results for gland segmentation, since the characteristics
and problems of glandular datasets are different from general object datasets.
We observe that, unlike natural images, the key problem with histology images
is the confusion of classes owning to morphological homogeneity and low color
contrast among different tissues. To this end, we propose a novel method Online
Easy Example Mining (OEEM) that encourages the network to focus on credible
supervision signals rather than noisy signals, therefore mitigating the
influence of inevitable false predictions in pseudo-masks. According to the
characteristics of glandular datasets, we design a strong framework for gland
segmentation. Our results exceed many fully-supervised methods and
weakly-supervised methods for gland segmentation over 4.4% and 6.04% at mIoU,
respectively. Code is available at this https URL.

提出了一种在线易学例挖掘（OEEM）方法，该方法鼓励网络专注于可信的监督信号而非噪声信号，从而缓解了伪遮罩中不可避免的误报预测的影响，针对腺体数据集的特点，设计了强大的腺体分割框架，其结果在 mIoU 方面超过了许多全监督方法和弱监督方法使腺体分割方法性能提高了 4.4% 和 6.04%。

基于弱监督的组织学图像腺体分割的在线易例挖掘

Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images

Modeling hand-object manipulations is essential for understanding how humans
interact with their environment. While of practical importance, estimating the
pose of hands and objects during interactions is challenging due to the large
mutual occlusions that occur during manipulation. Recent efforts have been
directed towards fully-supervised methods that require large amounts of labeled
training samples. Collecting 3D ground-truth data for hand-object interactions,
however, is costly, tedious, and error-prone. To overcome this challenge we
present a method to leverage photometric consistency across time when
annotations are only available for a sparse subset of frames in a video. Our
model is trained end-to-end on color images to jointly reconstruct hands and
objects in 3D by inferring their poses. Given our estimated reconstructions, we
differentiably render the optical flow between pairs of adjacent images and use
it within the network to warp one frame to another. We then apply a
self-supervised photometric loss that relies on the visual consistency between
nearby images. We achieve state-of-the-art results on 3D hand-object
reconstruction benchmarks and demonstrate that our approach allows us to
improve the pose estimation accuracy by leveraging information from neighboring
frames in low-data regimes.

模拟手 - 物互动的姿态估计方法，通过不依赖稠密标注数据的光度一致性方法可同时进行手和物体的三维重建和精确定位，取得了 3D 手 - 物重建基准测试的最优结果，并展示了其在低数据场景下通过相邻帧获取信息以提高姿态估计精度的能力。