Deep learning for detecting objects in remotely sensed imagery can enable new
technologies for important applications including mitigating climate change.
However, these models often require large datasets labeled with bounding box
annotations which are expensive to curate, prohibiting the development of
models for new tasks and geographies. To address this challenge, we develop
weakly-semi-supervised object detection (WSSOD) models on remotely sensed
imagery which can leverage a small amount of bounding boxes together with a
large amount of point labels that are easy to acquire at scale in geospatial
data. We train WSSOD models which use large amounts of point-labeled images
with varying fractions of bounding box labeled images in FAIR1M and a wind
turbine detection dataset, and demonstrate that they substantially outperform
fully supervised models trained with the same amount of bounding box labeled
images on both datasets. Furthermore, we find that the WSSOD models trained
with 2-10x fewer bounding box labeled images can perform similarly to or
outperform fully supervised models trained on the full set of bounding-box
labeled images. We believe that the approach can be extended to other remote
sensing tasks to reduce reliance on bounding box labels and increase
development of models for impactful applications.

利用弱监督的深度学习模型和少量边界框标记图像，可以在遥感图像中检测物体，从而减少对边界框标记的依赖，提高模型性能。

遥感图像中的弱半监督目标检测

Weakly-semi-supervised object detection in remotely sensed imagery

Current 3D segmentation methods heavily rely on large-scale point-cloud
datasets, which are notoriously laborious to annotate. Few attempts have been
made to circumvent the need for dense per-point annotations. In this work, we
look at weakly-supervised 3D semantic instance segmentation. The key idea is to
leverage 3D bounding box labels which are easier and faster to annotate.
Indeed, we show that it is possible to train dense segmentation models using
only bounding box labels. At the core of our method, \name{}, lies a deep
model, inspired by classical Hough voting, that directly votes for bounding box
parameters, and a clustering method specifically tailored to bounding box
votes. This goes beyond commonly used center votes, which would not fully
exploit the bounding box annotations. On ScanNet test, our weakly supervised
model attains leading performance among other weakly supervised approaches (+18
mAP@50). Remarkably, it also achieves 97% of the mAP@50 score of current fully
supervised models. To further illustrate the practicality of our work, we train
Box2Mask on the recently released ARKitScenes dataset which is annotated with
3D bounding boxes only, and show, for the first time, compelling 3D instance
segmentation masks.

本文提出 Box2Mask 方法，利用 3D 包围盒标签进行弱监督学习，通过 Hough 投票和聚类方法实现 3D 实例分割，无需密集注释点云数据，模型在 ScanNet 测试上达到领先水平（+18 mAP@50），并在实现 3D 实例分割掩码方面取得了令人信服的结果。

Box2Mask: 使用边界框进行弱监督的三维语义实例分割

Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using  Bounding Boxes

Building a large image dataset with high-quality object masks for semantic
segmentation is costly and time consuming. In this paper, we introduce a
principled semi-supervised framework that only uses a small set of fully
supervised images (having semantic segmentation labels and box labels) and a
set of images with only object bounding box labels (we call it the weak set).
Our framework trains the primary segmentation model with the aid of an
ancillary model that generates initial segmentation labels for the weak set and
a self-correction module that improves the generated labels during training
using the increasingly accurate primary model. We introduce two variants of the
self-correction module using either linear or convolutional functions.
Experiments on the PASCAL VOC 2012 and Cityscape datasets show that our models
trained with a small fully supervised set perform similar to, or better than,
models trained with a large fully supervised set while requiring ~7x less
annotation effort.

本研究介绍了一种半监督框架，通过一个辅助模型和一个自我纠正模块，在只有一小部分完全有监督图像的基础上，使用具有目标边界框标签的图像和只有目标边界框标签的图像集（称为弱集），训练出高质量的语义分割模型，这种方法比传统大量完全有监督数据模型要求的标注工作量减少～7 倍。