  Semantic video segmentation is a key challenge for various applications. This
paper presents a new model named Noisy-LSTM, which is trainable in an
end-to-end manner, with convolutional LSTMs (ConvLSTMs) to leverage the
temporal coherency in video frames. We also present a simple yet effective
training strategy, which replaces a frame in video sequence with noises. This
strategy spoils the temporal coherency in video frames during training and thus
makes the temporal links in ConvLSTMs unreliable, which may consequently
improve feature extraction from video frames, as well as serve as a regularizer
to avoid overfitting, without requiring extra data annotation or computational
costs. Experimental results demonstrate that the proposed model can achieve
state-of-the-art performances in both the CityScapes and EndoVis2018 datasets.
