Benefiting from the capability of building inter-dependencies among channels
or spatial locations, attention mechanisms have been extensively studied and
broadly used in a variety of computer vision tasks recently. In this paper, we
investigate light-weight but effective attention mechanisms and present triplet
attention, a novel method for computing attention weights by capturing
cross-dimension interaction using a three-branch structure. For an input
tensor, triplet attention builds inter-dimensional dependencies by the rotation
operation followed by residual transformations and encodes inter-channel and
spatial information with negligible computational overhead. Our method is
simple as well as efficient and can be easily plugged into classic backbone
networks as an add-on module. We demonstrate the effectiveness of our method on
various challenging tasks including image classification on ImageNet-1k and
object detection on MSCOCO and PASCAL VOC datasets. Furthermore, we provide
extensive in-sight into the performance of triplet attention by visually
inspecting the GradCAM and GradCAM++ results. The empirical evaluation of our
method supports our intuition on the importance of capturing dependencies
across dimensions when computing attention weights. Code for this paper can be
publicly accessed at this https URL

本文提出了一个计算注意力加权值的新方法－三元注意力，通过旋转操作建立了跨维度交互关系以及通过残差变换编码跨通道和空间信息，能够轻量级且高效地嵌入到经典的后骨干网络中。实验结果表明，我们的方法在图像分类以及物体检测等方面比其他注意力机制更加有效。