Autonomous vehicles utilize urban scene segmentation to understand the real
world like a human and react accordingly. Semantic segmentation of normal
scenes has experienced a remarkable rise in accuracy on conventional
benchmarks. However, a significant portion of real-life accidents features
abnormal scenes, such as those with object deformations, overturns, and
unexpected traffic behaviors. Since even small mis-segmentation of driving
scenes can lead to serious threats to human lives, the robustness of such
models in accident scenarios is an extremely important factor in ensuring
safety of intelligent transportation systems.
In this paper, we propose a Multi-source Meta-learning Unsupervised Domain
Adaptation (MMUDA) framework, to improve the generalization of segmentation
transformers to extreme accident scenes. In MMUDA, we make use of Multi-Domain
Mixed Sampling to augment the images of multiple-source domains (normal scenes)
with the target data appearances (abnormal scenes). To train our model, we
intertwine and study a meta-learning strategy in the multi-source setting for
robustifying the segmentation results. We further enhance the segmentation
backbone (SegFormer) with a HybridASPP decoder design, featuring large window
attention spatial pyramid pooling and strip pooling, to efficiently aggregate
long-range contextual dependencies. Our approach achieves a mIoU score of
46.97% on the DADA-seg benchmark, surpassing the previous state-of-the-art
model by more than 7.50%. Code will be made publicly available at
this https URL

本文提出一种多源元学习无监督域自适应框架（MMUDA）来提高分割转换器到极端事故场景的泛化能力，增强分割骨架，并在 DADA-seg 基准测试中实现 46.97% 的 mIoU 分数，超过先前最先进的模型 7.50% 以上。

通过多源混合采样和元学习实现事故场景的稳健语义分割

Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source  Mixed Sampling and Meta-Learning

Supervised learning in large discriminative models is a mainstay for modern
computer vision. Such an approach necessitates investing in large-scale
human-annotated datasets for achieving state-of-the-art results. In turn, the
efficacy of supervised learning may be limited by the size of the human
annotated dataset. This limitation is particularly notable for image
segmentation tasks, where the expense of human annotation is especially large,
yet large amounts of unlabeled data may exist. In this work, we ask if we may
leverage semi-supervised learning in unlabeled video sequences and extra images
to improve the performance on urban scene segmentation, simultaneously tackling
semantic, instance, and panoptic segmentation. The goal of this work is to
avoid the construction of sophisticated, learned architectures specific to
label propagation (e.g., patch matching and optical flow). Instead, we simply
predict pseudo-labels for the unlabeled data and train subsequent models with
both human-annotated and pseudo-labeled data. The procedure is iterated for
several times. As a result, our Naive-Student model, trained with such simple
yet effective iterative semi-supervised learning, attains state-of-the-art
results at all three Cityscapes benchmarks, reaching the performance of 67.8%
PQ, 42.6% AP, and 85.2% mIOU on the test set. We view this work as a notable
step towards building a simple procedure to harness unlabeled video sequences
and extra images to surpass state-of-the-art performance on core computer
vision tasks.

通过在未标注的视频序列和额外图像上使用半监督学习，结合人工标注标签和伪标签数据进行训练，该简单而有效的迭代半监督学习方法在城市街景分割任务上取得了 67.8％的 PQ，42.6％的 AP 和 85.2％的 mIOU 的最优成果，并在核心计算机视觉任务上实现了超越最先进水平的重要进展。

利用半监督学习进行城市场景分割的 Naive-Student 方法

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences  for Urban Scene Segmentation

A domain adaptation method for urban scene segmentation is proposed in this
work. We develop a fully convolutional tri-branch network, where two branches
assign pseudo labels to images in the unlabeled target domain while the third
branch is trained with supervision based on images in the pseudo-labeled target
domain. The re-labeling and re-training processes alternate. With this design,
the tri-branch network learns target-specific discriminative representations
progressively and, as a result, the cross-domain capability of the segmenter
improves. We evaluate the proposed network on large-scale domain adaptation
experiments using both synthetic (GTA) and real (Cityscapes) images. It is
shown that our solution achieves the state-of-the-art performance and it
outperforms previous methods by a significant margin.

本文提出了一种城市场景分割的域自适应方法，通过设计三分支网络，其中两个支路对未标注的目标域图像进行伪标签分配，第三个支路根据伪标签目标域图像进行监督训练，通过反复的重新标注和重新训练，该网络逐渐学习到目标特定的辨别性表示，从而改善了分割器的跨域能力，在基于大规模合成（GTA）和真实（Cityscapes）图像的域自适应实验中表现出卓越的前沿水平，优于先前的方法。