The convolution layer has been the dominant feature extractor in computer
vision for years. However, the spatial aggregation in convolution is basically
a pattern matching process that applies fixed filters which are inefficient at
modeling visual elements with varying spatial distributions. This paper
presents a new image feature extractor, called the local relation layer, that
adaptively determines aggregation weights based on the compositional
relationship of local pixel pairs. With this relational approach, it can
composite visual elements into higher-level entities in a more efficient manner
that benefits semantic inference. A network built with local relation layers,
called the Local Relation Network (LR-Net), is found to provide greater
modeling capacity than its counterpart built with regular convolution on
large-scale recognition tasks such as ImageNet classification.

本文提出了一种新的图像特征提取器 —— 局部关系层，它基于局部像素对的组合关系自适应地确定聚合权重，从而更高效地将视觉元素组合成更高级别实体，提供更好的语义推理，通过局部关系层构建的神经网络（LR-Net）在大规模识别任务中具有比其卷积网络更大的建模能力，如 ImageNet 分类。