Complex video object segmentation serves as a fundamental task for a wide
range of downstream applications such as video editing and automatic data
annotation. Here we present the 2nd place solution in the MOSE track of PVUW
2024. To mitigate problems caused by tiny objects, similar objects and fast
movements in MOSE. We use instance segmentation to generate extra pretraining
data from the valid and test set of MOSE. The segmented instances are combined
with objects extracted from COCO to augment the training data and enhance
semantic representation of the baseline model. Besides, motion blur is added
during training to increase robustness against image blur induced by motion.
Finally, we apply test time augmentation (TTA) and memory strategy to the
inference stage. Our method ranked 2nd in the MOSE track of PVUW 2024, with a
$\mathcal{J}$ of 0.8007, a $\mathcal{F}$ of 0.8683 and a
$\mathcal{J}$\&$\mathcal{F}$ of 0.8345.

我们在 MOSE 轨道的 PVUW 2024 比赛中排名第二，并采用实例分割、运动模糊、测试时间增强和记忆策略等方法来提高视频对象分割的准确性。

CVPR2024 PVUW 工作坊 MOSE 跟踪比赛第二名解决方案：复杂视频目标分割

2nd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex  Video Object Segmentation

Reinforcement learning (RL) agents performing complex tasks must be able to
remember observations and actions across sizable time intervals. This is
especially true during the initial learning stages, when exploratory behaviour
can increase the delay between specific actions and their effects. Many new or
popular approaches for learning these distant correlations employ
backpropagation through time (BPTT), but this technique requires storing
observation traces long enough to span the interval between cause and effect.
Besides memory demands, learning dynamics like vanishing gradients and slow
convergence due to infrequent weight updates can reduce BPTT's practicality;
meanwhile, although online recurrent network learning is a developing topic,
most approaches are not efficient enough to use as replacements. We propose a
simple, effective memory strategy that can extend the window over which BPTT
can learn without requiring longer traces. We explore this approach empirically
on a few tasks and discuss its implications.

本文提出了一种使用简单且有效的记忆策略来扩展反向传播时间的窗口而不需要更长跟踪的方法，并在一些任务中进行了实证探讨。