  Event-based semantic segmentation has gained popularity due to its capability
to deal with scenarios under high-speed motion and extreme lighting conditions,
which cannot be addressed by conventional RGB cameras. Since it is hard to
annotate event data, previous approaches rely on event-to-image reconstruction
to obtain pseudo labels for training. However, this will inevitably introduce
noise, and learning from noisy pseudo labels, especially when generated from a
single source, may reinforce the errors. This drawback is also called
confirmation bias in pseudo-labeling. In this paper, we propose a novel hybrid
pseudo-labeling framework for unsupervised event-based semantic segmentation,
HPL-ESS, to alleviate the influence of noisy pseudo labels. In particular, we
first employ a plain unsupervised domain adaptation framework as our baseline,
which can generate a set of pseudo labels through self-training. Then, we
incorporate offline event-to-image reconstruction into the framework, and
obtain another set of pseudo labels by predicting segmentation maps on the
reconstructed images. A noisy label learning strategy is designed to mix the
two sets of pseudo labels and enhance the quality. Moreover, we propose a soft
prototypical alignment module to further improve the consistency of target
domain features. Extensive experiments show that our proposed method
outperforms existing state-of-the-art methods by a large margin on the
DSEC-Semantic dataset (+5.88% accuracy, +10.32% mIoU), which even surpasses
several supervised methods.
