论文标题
基于注意的对象和语义部分的联合检测
Attention-based Joint Detection of Object and Semantic Part
论文作者
论文摘要
在本文中，我们解决了对狗及其语义部分（如面部，腿等）的对象的联合检测的问题。我们的模型是在两个更快的RCNN模型的基础上创建的，它们共享其功能，以执行相关对象的基于新颖的注意力融合和零件功能，以获得两者的增强表示。这些表示形式用于两种模型的最终分类和边界框回归。我们在Pascal-Part 2010数据集上进行的实验表明，联合检测可以同时根据= 0.5的平均平均精度（MAP）来改善对象检测和零件检测。
In this paper, we address the problem of joint detection of objects like dog and its semantic parts like face, leg, etc. Our model is created on top of two Faster-RCNN models that share their features to perform a novel Attention-based feature fusion of related Object and Part features to get enhanced representations of both. These representations are used for final classification and bounding box regression separately for both models. Our experiments on the PASCAL-Part 2010 dataset show that joint detection can simultaneously improve both object detection and part detection in terms of mean Average Precision (mAP) at IoU=0.5.
