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.

在这篇论文中，我们提出了一种新颖的 RoI 特征提取器 Semantic RoI Align（SRA），它能够在各种变换下提取不变的 RoI 特征，用于两阶段检测器。我们通过引入语义注意力模块，利用 RoI 内的全局和局部语义关系自适应地确定不同采样区域。我们还提出了动态特征采样器 Dynamic Feature Sampler，根据 RoI 的宽高比动态采样特征，以提高 SRA 的效率。另外，我们引入了一种新的位置嵌入 Area Embedding，通过改进采样区域表示提供更准确的位置信息给 SRA。实验证明，我们的模型在比基准模型轻微计算开销的情况下显著优于其他模型，并具有出色的泛化能力，可以改善各种最先进的主干网络和检测方法的性能。