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SYSTEM AND METHOD FOR A UNIFIED ARCHITECTURE MULTI-TASK DEEP LEARNING MACHINE FOR OBJECT RECOGNITION

机译:用于对象识别的统一架构多任务深学习机的系统和方法

摘要

A system to recognize objects in an image includes an object detection network outputs a first hierarchical-calculated feature for a detected object. A face alignment regression network determines a regression loss for alignment parameters based on the first hierarchical-calculated feature. A detection box regression network determines a regression loss for detected boxes based on the first hierarchical-calculated feature. The object detection network further includes a weighted loss generator to generate a weighted loss for the first hierarchical-calculated feature, the regression loss for the alignment parameters and the regression loss of the detected boxes. A backpropagator backpropagates the generated weighted loss. A grouping network forms, based on the first hierarchical-calculated feature, the regression loss for the alignment parameters and the bounding box regression loss, at least one of a box grouping, an alignment parameter grouping, and a non-maximum suppression of the alignment parameters and the detected boxes.
机译:识别图像中的物体的系统包括物体检测网络,该网络为检测到的物体输出第一层次计算的特征。面部对齐回归网络基于第一分层计算的特征来确定对齐参数的回归损失。检测框回归网络基于第一分层计算的特征来确定检测框的回归损失。物体检测网络还包括加权损失生成器,以生成第一层次计算特征的加权损失,对准参数的回归损失和检测到的盒子的回归损失。反向传播器反向传播所产生的加权损失。分组网络基于第一层次计算的特征,对齐参数的回归损失和边界框回归损失,框分组,对齐参数分组和对齐的非最大抑制中的至少一个参数和检测到的框。

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