This paper proposes a Robust and Efficient Memory Network, referred to as
REMN, for studying semi-supervised video object segmentation (VOS).
Memory-based methods have recently achieved outstanding VOS performance by
performing non-local pixel-wise matching between the query and memory. However,
these methods have two limitations. 1) Non-local matching could cause
distractor objects in the background to be incorrectly segmented. 2) Memory
features with high temporal redundancy consume significant computing resources.
For limitation 1, we introduce a local attention mechanism that tackles the
background distraction by enhancing the features of foreground objects with the
previous mask. For limitation 2, we first adaptively decide whether to update
the memory features depending on the variation of foreground objects to reduce
temporal redundancy. Second, we employ a dynamic memory bank, which uses a
lightweight and differentiable soft modulation gate to decide how many memory
features need to be removed in the temporal dimension. Experiments demonstrate
that our REMN achieves state-of-the-art results on DAVIS 2017, with a
$\mathcal{J\&F}$ score of 86.3% and on YouTube-VOS 2018, with a $\mathcal{G}$
over mean of 85.5%. Furthermore, our network shows a high inference speed of
25+ FPS and uses relatively few computing resources.

该研究提出 REMN，一种稳健高效的记忆网络，用于半监督视频物体分割，通过引入本地注意机制和动态记忆库，解决了非局部匹配和高时间冗余的问题，实验表明 REMN 在 DAVIS 2017 和 YouTube-VOS 2018 上均取得最新的成果，具有较高的推理速度和相对较少的计算资源。

视频物体分割的鲁棒高效记忆网络

Robust and Efficient Memory Network for Video Object Segmentation

We present a generalization of the person-image generation task, in which a
human image is generated conditioned on a target pose and a set X of source
appearance images. In this way, we can exploit multiple, possibly complementary
images of the same person which are usually available at training and at
testing time. The solution we propose is mainly based on a local attention
mechanism which selects relevant information from different source image
regions, avoiding the necessity to build specific generators for each specific
cardinality of X. The empirical evaluation of our method shows the practical
interest of addressing the person-image generation problem in a multi-source
setting.

我们提出了一个通用的人像生成任务的泛化方法，通过目标姿势和一组源外观图像生成一个人像，基于本地关注机制选择不同源图像区域的相关信息，避免了为每个特定的 X 基数构建特定生成器的必要性，并通过实证评估验证了在多源环境中解决人像生成问题的实用性。