Feature fusion plays a crucial role in unconstrained face recognition where
inputs (probes) comprise of a set of $N$ low quality images whose individual
qualities vary. Advances in attention and recurrent modules have led to feature
fusion that can model the relationship among the images in the input set.
However, attention mechanisms cannot scale to large $N$ due to their quadratic
complexity and recurrent modules suffer from input order sensitivity. We
propose a two-stage feature fusion paradigm, Cluster and Aggregate, that can
both scale to large $N$ and maintain the ability to perform sequential
inference with order invariance. Specifically, Cluster stage is a linear
assignment of $N$ inputs to $M$ global cluster centers, and Aggregation stage
is a fusion over $M$ clustered features. The clustered features play an
integral role when the inputs are sequential as they can serve as a
summarization of past features. By leveraging the order-invariance of
incremental averaging operation, we design an update rule that achieves
batch-order invariance, which guarantees that the contributions of early image
in the sequence do not diminish as time steps increase. Experiments on IJB-B
and IJB-S benchmark datasets show the superiority of the proposed two-stage
paradigm in unconstrained face recognition. Code and pretrained models are
available in this https URL

本文提出了一种两阶段的特征融合范式，Cluster 和 Aggregate，用于无约束人脸识别，它能够适应大规模输入，维护顺序不变性，并通过聚类特征进行序列推理。实验结果表明，所提出的范式在无约束人脸识别中具有优越性。

大规模探测的人脸识别：聚类和聚合

Cluster and Aggregate: Face Recognition with Large Probe Set

We propose Radial Bayesian Neural Networks (BNNs): a variational approximate
posterior for BNNs which scales well to large models while maintaining a
distribution over weight-space with full support. Other scalable Bayesian deep
learning methods, like MC dropout or deep ensembles, have discrete support-they
assign zero probability to almost all of the weight-space. Unlike these
discrete support methods, Radial BNNs' full support makes them suitable for use
as a prior for sequential inference. In addition, they solve the conceptual
challenges with the a priori implausibility of weight distributions with
discrete support. The Radial BNN is motivated by avoiding a sampling problem in
'mean-field' variational inference (MFVI) caused by the so-called 'soap-bubble'
pathology of multivariate Gaussians. We show that, unlike MFVI, Radial BNNs are
robust to hyperparameters and can be efficiently applied to a challenging
real-world medical application without needing ad-hoc tweaks and intensive
tuning. In fact, in this setting Radial BNNs out-perform discrete-support
methods like MC dropout. Lastly, by using Radial BNNs as a theoretically
principled, robust alternative to MFVI we make significant strides in a
Bayesian continual learning evaluation.

我们提出了径向贝叶斯神经网络（Radial Bayesian Neural Networks，简称 Radial BNNs）: 一种变分后验的 BNNs，它适用于大型模型而仍然保持对权重空间的完全支持的分布。与其他可扩展的贝叶斯深度学习方法不同，如 MC dropout 或深度集合，Radial BNNs 的全面支持使它们适用于顺序推理的先验。此外，它们解决了具有离散支持的权重分布的先验不合理性的概念性挑战。通过使用 Radial BNNs 作为相对于 MFVI (稳定均场方差近似) 的一种理论上合理、稳健的替代方案，我们在贝叶斯连续学习评估中取得了重大进展。

基于径向函数的贝叶斯神经网络：在大规模贝叶斯深度学习中突破离散支持限制

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale  Bayesian Deep Learning

Rich semantic relations are important in a variety of visual recognition
problems. As a concrete example, group activity recognition involves the
interactions and relative spatial relations of a set of people in a scene.
State of the art recognition methods center on deep learning approaches for
training highly effective, complex classifiers for interpreting images.
However, bridging the relatively low-level concepts output by these methods to
interpret higher-level compositional scenes remains a challenge. Graphical
models are a standard tool for this task. In this paper, we propose a method to
integrate graphical models and deep neural networks into a joint framework.
Instead of using a traditional inference method, we use a sequential inference
modeled by a recurrent neural network. Beyond this, the appropriate structure
for inference can be learned by imposing gates on edges between nodes.
Empirical results on group activity recognition demonstrate the potential of
this model to handle highly structured learning tasks.

本文提出了一种将图形模型和深度神经网络集成到一个联合框架中的方法，在序列推断方面采用递归神经网络的建模，通过对节点之间的边缘施加门来学习推断的适当结构。实证研究表明，这种模型具有处理高度结构化学习任务的潜力，再现了在群体活动识别问题的表现。