The scarcity of labeled data in real-world scenarios is a critical bottleneck
of deep learning's effectiveness. Semi-supervised semantic segmentation has
been a typical solution to achieve a desirable tradeoff between annotation cost
and segmentation performance. However, previous approaches, whether based on
consistency regularization or self-training, tend to neglect the contextual
knowledge embedded within inter-pixel relations. This negligence leads to
suboptimal performance and limited generalization. In this paper, we propose a
novel approach IPixMatch designed to mine the neglected but valuable
Inter-Pixel information for semi-supervised learning. Specifically, IPixMatch
is constructed as an extension of the standard teacher-student network,
incorporating additional loss terms to capture inter-pixel relations. It shines
in low-data regimes by efficiently leveraging the limited labeled data and
extracting maximum utility from the available unlabeled data. Furthermore,
IPixMatch can be integrated seamlessly into most teacher-student frameworks
without the need of model modification or adding additional components. Our
straightforward IPixMatch method demonstrates consistent performance
improvements across various benchmark datasets under different partitioning
protocols.

IPixMatch 是一种用于半监督学习的新方法，通过挖掘被忽视但有价值的像素间信息，以最大化利用有限的标记数据和提取未标记数据的最大效用，实现了在各种基准数据集中一致的性能改进。

IPixMatch: 提升半监督语义分割的互像素关系

IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel  Relation

This paper presents a novel approach for learning instance segmentation with
image-level class labels as supervision. Our approach generates pseudo instance
segmentation labels of training images, which are used to train a fully
supervised model. For generating the pseudo labels, we first identify confident
seed areas of object classes from attention maps of an image classification
model, and propagate them to discover the entire instance areas with accurate
boundaries. To this end, we propose IRNet, which estimates rough areas of
individual instances and detects boundaries between different object classes.
It thus enables to assign instance labels to the seeds and to propagate them
within the boundaries so that the entire areas of instances can be estimated
accurately. Furthermore, IRNet is trained with inter-pixel relations on the
attention maps, thus no extra supervision is required. Our method with IRNet
achieves an outstanding performance on the PASCAL VOC 2012 dataset, surpassing
not only previous state-of-the-art trained with the same level of supervision,
but also some of previous models relying on stronger supervision.

本文提出了一种新颖的方法，使用图像级别的类标签作为监督来学习实例分割。我们的方法生成训练图像的伪实例分割标签，并用于训练完全监督模型。通过 IRNet，我们首先从图像分类模型的 attention maps 中识别出与对象类别相关的种子区域，然后传播到整个实例区域，估计出准确的边界，并为种子分配实例标签，使得整个实例区域可以准确地估计。IRNet 是基于 attention maps 的像素间关系训练的，因此不需要额外的监督。在 PASCAL VOC 2012 数据集上，我们的方法与 IRNet 取得了出色的表现，不仅超过了之前使用相同级别监督训练的最先进模型，还超过了一些依赖于更强监督的先前模型。