Predicting future motion based on historical motion sequence is a fundamental
problem in computer vision, and it has wide applications in autonomous driving
and robotics. Some recent works have shown that Graph Convolutional
Networks(GCN) are instrumental in modeling the relationship between different
joints. However, considering the variants and diverse action types in human
motion data, the cross-dependency of the spatio-temporal relationships will be
difficult to depict due to the decoupled modeling strategy, which may also
exacerbate the problem of insufficient generalization. Therefore, we propose
the Spatio-Temporal Gating-Adjacency GCN(GAGCN) to learn the complex
spatio-temporal dependencies over diverse action types. Specifically, we adopt
gating networks to enhance the generalization of GCN via the trainable adaptive
adjacency matrix obtained by blending the candidate spatio-temporal adjacency
matrices. Moreover, GAGCN addresses the cross-dependency of space and time by
balancing the weights of spatio-temporal modeling and fusing the decoupled
spatio-temporal features. Extensive experiments on Human 3.6M, AMASS, and 3DPW
demonstrate that GAGCN achieves state-of-the-art performance in both short-term
and long-term predictions.

提出了一种名为 Spatio-Temporal Gating-Adjacency GCN (GAGCN) 的运动预测模型，采用门控网络增强了 GCN 对于多种动作类型的推广能力，并通过平衡空间和时间的交叉依赖关系，融合了模型的特征，实现了对于人体运动预测的短期和长期性能的最先进水平。

基于时空门限 - 邻接图卷积网络的人体运动预测

Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction

Age estimation is an important yet very challenging problem in computer
vision. Existing methods for age estimation usually apply a divide-and-conquer
strategy to deal with heterogeneous data caused by the non-stationary aging
process. However, the facial aging process is also a continuous process, and
the continuity relationship between different components has not been
effectively exploited. In this paper, we propose BridgeNet for age estimation,
which aims to mine the continuous relation between age labels effectively. The
proposed BridgeNet consists of local regressors and gating networks. Local
regressors partition the data space into multiple overlapping subspaces to
tackle heterogeneous data and gating networks learn continuity aware weights
for the results of local regressors by employing the proposed bridge-tree
structure, which introduces bridge connections into tree models to enforce the
similarity between neighbor nodes. Moreover, these two components of BridgeNet
can be jointly learned in an end-to-end way. We show experimental results on
the MORPH II, FG-NET and Chalearn LAP 2015 datasets and find that BridgeNet
outperforms the state-of-the-art methods.

本文提出了 BridgeNet 方法用于年龄预测，通过利用连续关系和门控网络来解决面部年龄变化的连续性问题并达到更好的效果。