We propose a novel solution for semi-supervised video object segmentation. By
the nature of the problem, available cues (e.g. video frame(s) with object
masks) become richer with the intermediate predictions. However, the existing
methods are unable to fully exploit this rich source of information. We resolve
the issue by leveraging memory networks and learn to read relevant information
from all available sources. In our framework, the past frames with object masks
form an external memory, and the current frame as the query is segmented using
the mask information in the memory. Specifically, the query and the memory are
densely matched in the feature space, covering all the space-time pixel
locations in a feed-forward fashion. Contrast to the previous approaches, the
abundant use of the guidance information allows us to better handle the
challenges such as appearance changes and occlussions. We validate our method
on the latest benchmark sets and achieved the state-of-the-art performance
(overall score of 79.4 on Youtube-VOS val set, J of 88.7 and 79.2 on DAVIS
2016/2017 val set respectively) while having a fast runtime (0.16 second/frame
on DAVIS 2016 val set).

提出了一种用于半监督视频对象分割的新型解决方案，通过利用内存网络中的可用线索和遍历所有可用源中的相关信息来实现更好的处理外观变化和遮挡等挑战，从而实现了最先进的性能。

利用时空记忆网络进行视频对象分割

Video Object Segmentation using Space-Time Memory Networks

This paper proposes a crowd counting method. Crowd counting is difficult
because of large appearance changes of a target which caused by density and
scale changes. Conventional crowd counting methods generally utilize one
predictor (e,g., regression and multi-class classifier). However, such only one
predictor can not count targets with large appearance changes well. In this
paper, we propose to predict the number of targets using multiple CNNs
specialized to a specific appearance, and those CNNs are adaptively selected
according to the appearance of a test image. By integrating the selected CNNs,
the proposed method has the robustness to large appearance changes. In
experiments, we confirm that the proposed method can count crowd with lower
counting error than a CNN and integration of CNNs with fixed weights. Moreover,
we confirm that each predictor automatically specialized to a specific
appearance.

本论文提出了一种群众计数方法，该方法使用多个针对特定外观的 CNN 进行预测，这些 CNN 根据测试图像的外观被自适应地选择，从而具有对大尺度外观变化的鲁棒性，并且可以比 CNN 和固定权重的集成方法更准确地计算群体数量。