Active Learning (AL) and Few Shot Learning (FSL) are two label-efficient
methods which have achieved excellent results recently. However, most prior
arts in both learning paradigms fail to explore the wealth of the vast
unlabelled data. In this study, we address this issue in the scenario where the
annotation budget is very limited, yet a large amount of unlabelled data for
the target task is available. We frame this work in the context of
histopathology where labelling is prohibitively expensive. To this end, we
introduce an active few shot learning framework, Myriad Active Learning (MAL),
including a contrastive-learning encoder, pseudo-label generation, and novel
query sample selection in the loop. Specifically, we propose to massage
unlabelled data in a self-supervised manner, where the obtained data
representations and clustering knowledge form the basis to activate the AL
loop. With feedback from the oracle in each AL cycle, the pseudo-labels of the
unlabelled data are refined by optimizing a shallow task-specific net on top of
the encoder. These updated pseudo-labels serve to inform and improve the active
learning query selection process. Furthermore, we introduce a novel recipe to
combine existing uncertainty measures and utilize the entire uncertainty list
to reduce sample redundancy in AL. Extensive experiments on two public
histopathology datasets show that MAL has superior test accuracy, macro
F1-score, and label efficiency compared to prior works, and can achieve a
comparable test accuracy to a fully supervised algorithm while labelling only
5% of the dataset.

在这项研究中，我们提出了一种主动少样本学习框架，名为 Myriad Active Learning（MAL），该框架利用对无标签数据的自我监督处理和聚类知识作为激活主动学习循环的基础，并通过优化编码器上的浅层任务网络来改善伪标签的生成和主动学习查询选择过程，进而减少样本的冗余性，并且在两个公共组织病理学数据集上的广泛实验证明，MAL 相较于之前的方法具有更高的测试准确性、宏 F1 分数和标签效率，并且能在只对数据集的 5% 进行标注的情况下达到与完全监督算法可比的测试准确性。

MyriadAL：组织病理学的主动少样本学习

MyriadAL: Active Few Shot Learning for Histopathology

Deep learning applications on LiDAR data suffer from a strong domain gap when
applied to different sensors or tasks. In order for these methods to obtain
similar accuracy on different data in comparison to values reported on public
benchmarks, a large scale annotated dataset is necessary. However, in practical
applications labeled data is costly and time consuming to obtain. Such factors
have triggered various research in label-efficient methods, but a large gap
remains to their fully-supervised counterparts. Thus, we propose ImageTo360, an
effective and streamlined few-shot approach to label-efficient LiDAR
segmentation. Our method utilizes an image teacher network to generate semantic
predictions for LiDAR data within a single camera view. The teacher is used to
pretrain the LiDAR segmentation student network, prior to optional fine-tuning
on 360$^\circ$ data. Our method is implemented in a modular manner on the point
level and as such is generalizable to different architectures. We improve over
the current state-of-the-art results for label-efficient methods and even
surpass some traditional fully-supervised segmentation networks.

我们提出了 ImageTo360，一种有效且简化的少样本标签高效 LiDAR 分割方法，利用图像教师网络在单个摄像机视角下为 LiDAR 数据生成语义预测，并使 LiDAR 分割学生网络进行预训练，从而改进了当前标签高效方法的结果并超越了一些传统的全监督分割网络。

单一相机的 360 度视角：用于 LiDAR 分割的少样本方法

360$^\circ$ from a Single Camera: A Few-Shot Approach for LiDAR  Segmentation

The past decade has witnessed many great successes of machine learning (ML)
and deep learning (DL) applications in agricultural systems, including weed
control, plant disease diagnosis, agricultural robotics, and precision
livestock management. Despite tremendous progresses, one downside of such ML/DL
models is that they generally rely on large-scale labeled datasets for
training, and the performance of such models is strongly influenced by the size
and quality of available labeled data samples. In addition, collecting,
processing, and labeling such large-scale datasets is extremely costly and
time-consuming, partially due to the rising cost in human labor. Therefore,
developing label-efficient ML/DL methods for agricultural applications has
received significant interests among researchers and practitioners. In fact,
there are more than 50 papers on developing and applying deep-learning-based
label-efficient techniques to address various agricultural problems since 2016,
which motivates the authors to provide a timely and comprehensive review of
recent label-efficient ML/DL methods in agricultural applications. To this end,
we first develop a principled taxonomy to organize these methods according to
the degree of supervision, including weak supervision (i.e., active learning
and semi-/weakly- supervised learning), and no supervision (i.e., un-/self-
supervised learning), supplemented by representative state-of-the-art
label-efficient ML/DL methods. In addition, a systematic review of various
agricultural applications exploiting these label-efficient algorithms, such as
precision agriculture, plant phenotyping, and postharvest quality assessment,
is presented. Finally, we discuss the current problems and challenges, as well
as future research directions. A well-classified paper list can be accessed at
this https URL

该论文综述了机器学习和深度学习在农业系统中的应用，并介绍了最近在农业应用领域提出的标签有效机器学习和深度学习方法，特别是弱监督、无监督和自监督学习方法，以及其在精准农业、植物表型鉴定和收获后质量评估等方面的应用。