Action detection plays an important role in high-level video understanding
and media interpretation. Many existing studies fulfill this spatio-temporal
localization by modeling the context, capturing the relationship of actors,
objects, and scenes conveyed in the video. However, they often universally
treat all the actors without considering the consistency and distinctness
between individuals, leaving much room for improvement. In this paper, we
explicitly highlight the identity information of the actors in terms of both
long-term and short-term context through a graph memory network, namely
identity-aware graph memory network (IGMN). Specifically, we propose the
hierarchical graph neural network (HGNN) to comprehensively conduct long-term
relation modeling within the same identity as well as between different ones.
Regarding short-term context, we develop a dual attention module (DAM) to
generate identity-aware constraint to reduce the influence of interference by
the actors of different identities. Extensive experiments on the challenging
AVA dataset demonstrate the effectiveness of our method, which achieves
state-of-the-art results on AVA v2.1 and v2.2.

通过具有层级结构的图神经网络和注意力机制，提出了一个对于同一身份和不同身份之间的长期关系建模以及身份感知的研究，以显式地突出演员的身份信息，并在 AVA 数据集上获得了最先进的结果。

面向身份感知的图记忆网络用于动作检测

Identity-aware Graph Memory Network for Action Detection

How to make a segmentation model efficiently adapt to a specific video and to
online target appearance variations are fundamentally crucial issues in the
field of video object segmentation. In this work, a graph memory network is
developed to address the novel idea of "learning to update the segmentation
model". Specifically, we exploit an episodic memory network, organized as a
fully connected graph, to store frames as nodes and capture cross-frame
correlations by edges. Further, learnable controllers are embedded to ease
memory reading and writing, as well as maintain a fixed memory scale. The
structured, external memory design enables our model to comprehensively mine
and quickly store new knowledge, even with limited visual information, and the
differentiable memory controllers slowly learn an abstract method for storing
useful representations in the memory and how to later use these representations
for prediction, via gradient descent. In addition, the proposed graph memory
network yields a neat yet principled framework, which can generalize well both
one-shot and zero-shot video object segmentation tasks. Extensive experiments
on four challenging benchmark datasets verify that our graph memory network is
able to facilitate the adaptation of the segmentation network for case-by-case
video object segmentation.

本文提出了一种基于图形记忆网络的视频目标分割模型，结合可学习控制器进行知识存储和自适应更新，以实现迎合特定视频和在线目标外观变化的自适应分割；大量实验证明该模型具有较强的泛化能力和应用价值。