Existing techniques to encode spatial invariance within deep convolutional
neural networks (CNNs) apply the same warping field to all the feature
channels. This does not account for the fact that the individual feature
channels can represent different semantic parts, which can undergo different
spatial transformations w.r.t. a canonical configuration. To overcome this
limitation, we introduce a learnable module, the volumetric transformer network
(VTN), that predicts channel-wise warping fields so as to reconfigure
intermediate CNN features spatially and channel-wisely. We design our VTN as an
encoder-decoder network, with modules dedicated to letting the information flow
across the feature channels, to account for the dependencies between the
semantic parts. We further propose a loss function defined between the warped
features of pairs of instances, which improves the localization ability of VTN.
Our experiments show that VTN consistently boosts the features' representation
power and consequently the networks' accuracy on fine-grained image recognition
and instance-level image retrieval.

本文介绍了一种学习型模块（VTN，即体积变形网络），以预测通道级扭曲场，从而在空间和通道层面上重新配置 CNN 特征，改善空间不变性技术在大规模对象识别中的限制。经实验证明，VTN 不断提升模型性能和图像识别和检索的精度。