  The superior performance of Deformable Convolutional Networks arises from its
ability to adapt to the geometric variations of objects. Through an examination
of its adaptive behavior, we observe that while the spatial support for its
neural features conforms more closely than regular ConvNets to object
structure, this support may nevertheless extend well beyond the region of
interest, causing features to be influenced by irrelevant image content. To
address this problem, we present a reformulation of Deformable ConvNets that
improves its ability to focus on pertinent image regions, through increased
modeling power and stronger training. The modeling power is enhanced through a
more comprehensive integration of deformable convolution within the network,
and by introducing a modulation mechanism that expands the scope of deformation
modeling. To effectively harness this enriched modeling capability, we guide
network training via a proposed feature mimicking scheme that helps the network
to learn features that reflect the object focus and classification power of
R-CNN features. With the proposed contributions, this new version of Deformable
ConvNets yields significant performance gains over the original model and
produces leading results on the COCO benchmark for object detection and
instance segmentation.
