Video-based person re-identification (reID) aims at matching the same person
across video clips. It is a challenging task due to the existence of redundancy
among frames, newly revealed appearance, occlusion, and motion blurs. In this
paper, we propose an attentive feature aggregation module, namely
Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to
delicately aggregate spatio-temporal features into a discriminative video-level
feature representation. In order to determine the contribution/importance of a
spatial-temporal feature node, we propose to learn the attention from a global
view with convolutional operations. Specifically, we stack its relations, i.e.,
pairwise correlations with respect to a representative set of reference feature
nodes (S-RFNs) that represents global video information, together with the
feature itself to infer the attention. Moreover, to exploit the semantics of
different levels, we propose to learn multi-granularity attentions based on the
relations captured at different granularities. Extensive ablation studies
demonstrate the effectiveness of our attentive feature aggregation module
MG-RAFA. Our framework achieves the state-of-the-art performance on three
benchmark datasets.

提出一种名为 MG-RAFA 的关注特征汇聚模块，用于将空间 - 时间特征精细地聚合成具有判别性的视频级别特征表示，采用全局视图与卷积操作学习关注，并基于不同粒度的关系学习多粒度关注，实现了视频 ReID 任务上的最新成果。

基于多粒度参考引导的注意力特征聚合技术用于基于视频的人员重识别

Multi-Granularity Reference-Aided Attentive Feature Aggregation for  Video-based Person Re-identification

We study the problem of recovering an underlying 3D shape from a set of
images. Existing learning based approaches usually resort to recurrent neural
nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to
fuse multiple deep features encoded from input images. However, GRU based
approaches are unable to consistently estimate 3D shapes given different
permutations of the same set of input images as the recurrent unit is
permutation variant. It is also unlikely to refine the 3D shape given more
images due to the long-term memory loss of GRU. Commonly used pooling
approaches are limited to capturing partial information, e.g., max/mean values,
ignoring other valuable features. In this paper, we present a new feed-forward
neural module, named AttSets, together with a dedicated training algorithm,
named FASet, to attentively aggregate an arbitrarily sized deep feature set for
multi-view 3D reconstruction. The AttSets module is permutation invariant,
computationally efficient and flexible to implement, while the FASet algorithm
enables the AttSets based network to be remarkably robust and generalize to an
arbitrary number of input images. We thoroughly evaluate FASet and the
properties of AttSets on multiple large public datasets. Extensive experiments
show that AttSets together with FASet algorithm significantly outperforms
existing aggregation approaches.

本文提出一种新的前馈神经模板 ——AttSets，以及一个专门的 FASet 训练算法，用于多视图的 3D 重建。AttSets 模块是置换不变的，计算效率高且易于实现，而 FASet 算法使基于 AttSets 的网络非常稳健，并且在多个公共数据集上确切地证明了 AttSets 与 FASet 算法的优越性。