Although group convolutional networks are able to learn powerful
representations based on symmetry patterns, they lack explicit means to learn
meaningful relationships among them (e.g., relative positions and poses). In
this paper, we present attentive group equivariant convolutions, a
generalization of the group convolution, in which attention is applied during
the course of convolution to accentuate meaningful symmetry combinations and
suppress non-plausible, misleading ones. We indicate that prior work on visual
attention can be described as special cases of our proposed framework and show
empirically that our attentive group equivariant convolutional networks
consistently outperform conventional group convolutional networks on benchmark
image datasets. Simultaneously, we provide interpretability to the learned
concepts through the visualization of equivariant attention maps.

提出了一种基于视觉注意力机制的等变卷积方法 AGECN，解决了对称模式下位置和姿态关系不明确的问题，实现了对图像数据集的高效表征。

关注组等变卷积网络

Attentive Group Equivariant Convolutional Networks

We present group equivariant capsule networks, a framework to introduce
guaranteed equivariance and invariance properties to the capsule network idea.
Our work can be divided into two contributions. First, we present a generic
routing by agreement algorithm defined on elements of a group and prove that
equivariance of output pose vectors, as well as invariance of output
activations, hold under certain conditions. Second, we connect the resulting
equivariant capsule networks with work from the field of group convolutional
networks. Through this connection, we provide intuitions of how both methods
relate and are able to combine the strengths of both approaches in one deep
neural network architecture. The resulting framework allows sparse evaluation
of the group convolution operator, provides control over specific equivariance
and invariance properties, and can use routing by agreement instead of pooling
operations. In addition, it is able to provide interpretable and equivariant
representation vectors as output capsules, which disentangle evidence of object
existence from its pose.

我们提出了一种基于群等变胶囊网络的框架，通过引入路由协议算法，实现了保证等变性和不变性的性质，在与群卷积网络相连的同时，提供了一种深度神经网络架构，可控制特定的等变性和不变性属性，并提供可解释和完全等变的表示向量作为输出胶囊。