Massive semantically labeled datasets are readily available for 2D images,
however, are much harder to achieve for 3D scenes. Objects in 3D repositories
like ShapeNet are labeled, but regrettably only in isolation, so without
context. 3D scenes can be acquired by range scanners on city-level scale, but
much fewer with semantic labels. Addressing this disparity, we introduce a new
optimization procedure, which allows training for 3D detection with raw 3D
scans while using as little as 5% of the object labels and still achieve
comparable performance. Our optimization uses two networks. A scene network
maps an entire 3D scene to a set of 3D object centers. As we assume the scene
not to be labeled by centers, no classic loss, such as Chamfer can be used to
train it. Instead, we use another network to emulate the loss. This loss
network is trained on a small labeled subset and maps a non centered 3D object
in the presence of distractions to its own center. This function is very
similar - and hence can be used instead of - the gradient the supervised loss
would provide. Our evaluation documents competitive fidelity at a much lower
level of supervision, respectively higher quality at comparable supervision.
Supplementary material can be found at: this https URL

通过在未经过场景中心标记的未经处理的 3D 扫描数据中使用尽可能少的目标标签进行训练，我们提出了一种新的优化程序，其中含有两个网络，它们可以在与监督有可比性的情况下实现相当的性能，从而解决了 2D 图像与 3D 场景之间的差异问题。

使用学习损失的 3D 目标检测：寻找你的（3D）中心

Finding Your (3D) Center: 3D Object Detection Using a Learned Loss

In this work, we investigate whether state-of-the-art object detection
systems have equitable predictive performance on pedestrians with different
skin tones. This work is motivated by many recent examples of ML and vision
systems displaying higher error rates for certain demographic groups than
others. We annotate an existing large scale dataset which contains pedestrians,
BDD100K, with Fitzpatrick skin tones in ranges [1-3] or [4-6]. We then provide
an in-depth comparative analysis of performance between these two skin tone
groupings, finding that neither time of day nor occlusion explain this
behavior, suggesting this disparity is not merely the result of pedestrians in
the 4-6 range appearing in more difficult scenes for detection. We investigate
to what extent time of day, occlusion, and reweighting the supervised loss
during training affect this predictive bias.

该研究研究了最新的物体检测系统是否能够在不同肤色的行人中具有公正的预测性能，我们对一个现有的大规模数据集 BDD100K 进行了注释，并提供了两种肤色分组之间性能的深入比较分析，发现时间、遮挡和训练期间重新加权的监督损失对这种预测性别扭曲的影响程度。

目标检测中的预测不公

Predictive Inequity in Object Detection

Accurate 3D human pose estimation from single images is possible with
sophisticated deep-net architectures that have been trained on very large
datasets. However, this still leaves open the problem of capturing motions for
which no such database exists. Manual annotation is tedious, slow, and
error-prone. In this paper, we propose to replace most of the annotations by
the use of multiple views, at training time only. Specifically, we train the
system to predict the same pose in all views. Such a consistency constraint is
necessary but not sufficient to predict accurate poses. We therefore complement
it with a supervised loss aiming to predict the correct pose in a small set of
labeled images, and with a regularization term that penalizes drift from
initial predictions. Furthermore, we propose a method to estimate camera pose
jointly with human pose, which lets us utilize multi-view footage where
calibration is difficult, e.g., for pan-tilt or moving handheld cameras. We
demonstrate the effectiveness of our approach on established benchmarks, as
well as on a new Ski dataset with rotating cameras and expert ski motion, for
which annotations are truly hard to obtain.

本文提出了一种通过多视角的训练，使用一致性约束联合监督损失及惩罚项实现替代大量标注的 3D 人体姿态估计方法，并提出了一种联合估计相机姿态及人体姿态的方法，在 3D 人体姿态估计方面取得了良好的表现。