  Great progress has been made in learning-based object detection methods in
the last decade. Two-stage detectors often have higher detection accuracy than
one-stage detectors, due to the use of region of interest (RoI) feature
extractors which extract transformation-invariant RoI features for different
RoI proposals, making refinement of bounding boxes and prediction of object
categories more robust and accurate. However, previous RoI feature extractors
can only extract invariant features under limited transformations. In this
paper, we propose a novel RoI feature extractor, termed Semantic RoI Align
(SRA), which is capable of extracting invariant RoI features under a variety of
transformations for two-stage detectors. Specifically, we propose a semantic
attention module to adaptively determine different sampling areas by leveraging
the global and local semantic relationship within the RoI. We also propose a
Dynamic Feature Sampler which dynamically samples features based on the RoI
aspect ratio to enhance the efficiency of SRA, and a new position embedding,
\ie Area Embedding, to provide more accurate position information for SRA
through an improved sampling area representation. Experiments show that our
model significantly outperforms baseline models with slight computational
overhead. In addition, it shows excellent generalization ability and can be
used to improve performance with various state-of-the-art backbones and
detection methods.
