Current state-of-the-art two-stage models on instance segmentation task
suffer from several types of imbalances. In this paper, we address the
Intersection over the Union (IoU) distribution imbalance of positive input
Regions of Interest (RoIs) during the training of the second stage. Our
Self-Balanced R-CNN (SBR-CNN), an evolved version of the Hybrid Task Cascade
(HTC) model, brings brand new loop mechanisms of bounding box and mask
refinements. With an improved Generic RoI Extraction (GRoIE), we also address
the feature-level imbalance at the Feature Pyramid Network (FPN) level,
originated by a non-uniform integration between low- and high-level features
from the backbone layers. In addition, the redesign of the architecture heads
toward a fully convolutional approach with FCC further reduces the number of
parameters and obtains more clues to the connection between the task to solve
and the layers used. Moreover, our SBR-CNN model shows the same or even better
improvements if adopted in conjunction with other state-of-the-art models. In
fact, with a lightweight ResNet-50 as backbone, evaluated on COCO minival 2017
dataset, our model reaches 45.3% and 41.5% AP for object detection and instance
segmentation, with 12 epochs and without extra tricks. The code is available at
this https URL

我们提出了一种名为自平衡 R-CNN (SBR-CNN) 的模型，通过解决实例分割任务中的不均衡问题，改进了目标检测和实例分割的性能，在 COCO minival 2017 数据集上达到了 45.3% 和 41.5% 的 AP。