论文标题
一个新型的感兴趣的提取层，例如分割
A novel Region of Interest Extraction Layer for Instance Segmentation
论文作者
论文摘要
鉴于深度神经网络体系结构用于计算机视觉任务的广泛扩散，如今几个新应用程序变得越来越可行。其中，最近通过利用从R-CNN得出的两个阶段网络（例如蒙版R-CNN或更快的R-CNN）来实现的结果，最近给予了特定的关注。在这些复杂的体系结构中，感兴趣的区域（ROI）提取层扮演着至关重要的角色，致力于从单个特征金字塔网络（FPN）层附着在骨干顶部的单个特征金字塔网络（FPN）层中提取一致的特征子集。 本文的激励是要克服仅从FPN中选择一个（最佳）层的现有ROI提取器的局限性。我们的直觉是，FPN的所有层都保留了有用的信息。因此，提出的层（称为通用ROI提取器-Groie）引入了非本地构建块和注意机制，以提高性能。 进行了组件级别的全面消融研究，以找到最佳的groie层算法和参数。此外，可以将Groie与每个两阶段体系结构无缝集成，以进行对象检测和实例分割任务。因此，还评估了在不同最先进的架构中使用谷物带来的改进。提出的层导致在边界框检测方面提高1.1％的AP提高，并在实例分段上提高了1.7％的AP提高。 该代码可在https://github.com/implabunipr/mmdetection/mmmdetection/tree/groie/groie_dev上公开获得。
Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extracting a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought about by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP improvement on bounding box detection and 1.7% AP improvement on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection/tree/groie_dev
