Recent self-training techniques have shown notable improvements in
unsupervised domain adaptation for 3D object detection (3D UDA). These
techniques typically select pseudo labels, i.e., 3D boxes, to supervise models
for the target domain. However, this selection process inevitably introduces
unreliable 3D boxes, in which 3D points cannot be definitively assigned as
foreground or background. Previous techniques mitigate this by reweighting
these boxes as pseudo labels, but these boxes can still poison the training
process. To resolve this problem, in this paper, we propose a novel pseudo
label refinery framework. Specifically, in the selection process, to improve
the reliability of pseudo boxes, we propose a complementary augmentation
strategy. This strategy involves either removing all points within an
unreliable box or replacing it with a high-confidence box. Moreover, the point
numbers of instances in high-beam datasets are considerably higher than those
in low-beam datasets, also degrading the quality of pseudo labels during the
training process. We alleviate this issue by generating additional proposals
and aligning RoI features across different domains. Experimental results
demonstrate that our method effectively enhances the quality of pseudo labels
and consistently surpasses the state-of-the-art methods on six autonomous
driving benchmarks. Code will be available at
this https URL

在这篇论文中，我们提出了一种新颖的伪标签精炼框架，通过补充性增强策略，生成额外的候选框并在不同领域中对 RoI 特征进行对齐，有效提高了伪标签的质量，并在六个自动驾驶基准测试中持续超越了现有最先进的方法。

未监督领域自适应 3D 物体检测的伪标签精炼

Pseudo Label Refinery for Unsupervised Domain Adaptation on  Cross-dataset 3D Object Detection

Semi-supervised learning has made remarkable strides by effectively utilizing
a limited amount of labeled data while capitalizing on the abundant information
present in unlabeled data. However, current algorithms often prioritize
aligning image predictions with specific classes generated through
self-training techniques, thereby neglecting the inherent relationships that
exist within these classes. In this paper, we present a new approach called
OTMatch, which leverages semantic relationships among classes by employing an
optimal transport loss function. By utilizing optimal transport, our proposed
method consistently outperforms established state-of-the-art methods. Notably,
we observed a substantial improvement of a certain percentage in accuracy
compared to the current state-of-the-art method, FreeMatch. OTMatch achieves
3.18%, 3.46%, and 1.28% error rate reduction over FreeMatch on CIFAR-10 with 1
label per class, STL-10 with 4 labels per class, and ImageNet with 100 labels
per class, respectively. This demonstrates the effectiveness and superiority of
our approach in harnessing semantic relationships to enhance learning
performance in a semi-supervised setting.

本文介绍了一种新的方法 OTMatch，通过使用最优输运损失函数，利用类别之间的语义关系，提高半监督学习的性能。与现有最先进的方法 FreeMatch 相比，在 CIFAR-10、STL-10 和 ImageNet 数据集上分别实现了 3.18%、3.46% 和 1.28% 的错误率降低，证明了我们方法在半监督学习中利用语义关系提升学习性能的有效性和优越性。

OTMatch：以最优传输方式改进半监督学习

OTMatch: Improving Semi-Supervised Learning with Optimal Transport

Models capable of leveraging unlabelled data are crucial in overcoming large
distribution gaps between the acquired datasets across different imaging
devices and configurations. In this regard, self-training techniques based on
pseudo-labeling have been shown to be highly effective for semi-supervised
domain adaptation. However, the unreliability of pseudo labels can hinder the
capability of self-training techniques to induce abstract representation from
the unlabeled target dataset, especially in the case of large distribution
gaps. Since the neural network performance should be invariant to image
transformations, we look to this fact to identify uncertain pseudo labels.
Indeed, we argue that transformation invariant detections can provide more
reasonable approximations of ground truth. Accordingly, we propose a
semi-supervised learning strategy for domain adaptation termed
transformation-invariant self-training (TI-ST). The proposed method assesses
pixel-wise pseudo-labels' reliability and filters out unreliable detections
during self-training. We perform comprehensive evaluations for domain
adaptation using three different modalities of medical images, two different
network architectures, and several alternative state-of-the-art domain
adaptation methods. Experimental results confirm the superiority of our
proposed method in mitigating the lack of target domain annotation and boosting
segmentation performance in the target domain.

半监督学习策略 —— 转化不变自训练（TI-ST）方法，通过评估像素级伪标签的可靠性并在自训练过程中过滤不可靠的检测，解决标签缺失和提高目标域分割性能。