基于反馈注意力机制和上下文融合的非模式实例分割

非模式实例分割是最近提出的对实例分割的扩展，其任务是对每个对象实例的可见区域和被遮挡区域都进行预测，感知完整的物理结构和语义概念。在预测对象被遮挡部分的形状和语义时，往往由于特征表示的识别能力不够和对上下文信息缺乏而导致对遮挡区域预测欠拟合甚至错误。针对这个问题，提出一个上下文注意模块和反馈注意力机制的特征金字塔结构，引入反馈连接进行再学习。该方法能够有效捕获全局语义信息和精细的空间细节，通过在COCO-amodal数据集训练和验证，非模式实例分割掩码平均精确率从8.4%提高到14.3%，平均召回率从16.6%提高到20.8%。实验结果表明，该方法能够显著提高对物体被遮挡部分预测的准确率，有效解决欠拟合问题。

Feedback attention mechanism and context fusion based amodal instance segmentation

Recently, model instance segmentation has been proposed as an extension of instance segmentation to predict the visible and occluded areas of each object instance and perceive the complete physical structure and semantic concepts. When the shapes and meanings of occluded objects are being predicted, underfitting or even wrong results are obtained in the occlusion prediction due to the insufficient recognition capability of feature representation and the lack of contextual information. To solve this problem, this paper proposes a contextual attention module and feature pyramid structure of feedback attention mechanism and introduces feedback connections for relearning. The proposed method can effectively capture global semantic information and fine spatial details. Through training and verification in the COCO-amodal dataset, the average precision of the amodal instance segmentation mask increases from 8.4% to 14.3%, and the average recall rate increases from 16.6% to 20.8%. Experimental results show that this method can significantly improve the accuracy of occlusion prediction and effectively end underfitting.