Title: Memory-Augmented Variational Adaptation for Online Few-Shot Segmentation. Abstract: We investigate online few-show segmentation, which learns to make dense predictions for novel classes while observing samples sequentially. The main challenge in such an online scenario is the sample diversity in the sequence, resulting in models that do not generalize well to future samples. To this end, we propose a memory-augmented variational adaptation mechanism, which learns to adapt the model to every new sample that arrives sequentially. Specifically, we first introduce a prototype memory, which retains category knowledge from previous samples to facilitate the model adaptation to future samples.  The adaptation to each new sample is then formulated as a variational Bayesian inference problem, which strives to generate sample-specific model parameters by conditioning the sample and the prototype memory. Furthermore, we propose memory-augmented  segmentation to learn sample-specific feature representation for better adaptation to the segmentation of each sample. With extensive experiments, we show that a simple extension of existing few-shot segmentation methods tends to converge to over-smoothed, averaged masks of lesser performance. By contrast, the proposed method achieves considerably better online few-shot segmentation performance.