Compared with tedious per-pixel mask annotating, it is much easier to
annotate data by clicks, which costs only several seconds for an image.
However, applying clicks to learn video semantic segmentation model has not
been explored before. In this work, we propose an effective weakly-supervised
video semantic segmentation pipeline with click annotations, called WeClick,
for saving laborious annotating effort by segmenting an instance of the
semantic class with only a single click. Since detailed semantic information is
not captured by clicks, directly training with click labels leads to poor
segmentation predictions. To mitigate this problem, we design a novel memory
flow knowledge distillation strategy to exploit temporal information (named
memory flow) in abundant unlabeled video frames, by distilling the neighboring
predictions to the target frame via estimated motion. Moreover, we adopt
vanilla knowledge distillation for model compression. In this case, WeClick
learns compact video semantic segmentation models with the low-cost click
annotations during the training phase yet achieves real-time and accurate
models during the inference period. Experimental results on Cityscapes and
Camvid show that WeClick outperforms the state-of-the-art methods, increases
performance by 10.24% mIoU than baseline, and achieves real-time execution.

本研究提出了一种基于点击注释的有效的弱监督视频语义分割管线 WeClick，使用了两种不同的知识蒸馏策略来学习模型并提升视频语义分割的效果，在实时执行方面表现较好。