  Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate
the burden of time-consuming pixel-level manual labeling, which leverages
limited labeled data along with larger amounts of unlabeled data. Current
state-of-the-art methods train the labeled data with ground truths and
unlabeled data with pseudo labels. However, the two training flows are
separate, which allows labeled data to dominate the training process, resulting
in low-quality pseudo labels and, consequently, sub-optimal results. To
alleviate this issue, we present AllSpark, which reborns the labeled features
from unlabeled ones with the channel-wise cross-attention mechanism. We further
introduce a Semantic Memory along with a Channel Semantic Grouping strategy to
ensure that unlabeled features adequately represent labeled features. The
AllSpark shed new light on the architecture level designs of SSSS rather than
framework level, which avoids increasingly complicated training pipeline
designs. It can also be regarded as a flexible bottleneck module that can be
seamlessly integrated into a general transformer-based segmentation model. The
proposed AllSpark outperforms existing methods across all evaluation protocols
on Pascal, Cityscapes and COCO benchmarks without bells-and-whistles. Code and
model weights are available at: https://github.com/xmed-lab/AllSpark.
