计算机科学
人工智能
班级(哲学)
相似性(几何)
交叉口(航空)
对象(语法)
基本事实
平面图(考古学)
骨干网
注释
模式识别(心理学)
机器学习
图像(数学)
工程类
地理
航空航天工程
考古
计算机网络
作者
Tahira Shehzadi,Khurram Azeem Hashmi,Alain Pagani,Marcus Liwicki,Didier Stricker,Muhammad Zeshan Afzal
摘要
Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labeled data. The floor-plan objects include different furniture items with multiple types of the same class, and this high inter-class similarity impacts the performance of prior methods. In this paper, we present Mask R-CNN-based semi-supervised approach that provides pixel-to-pixel alignment to generate individual annotation masks for each class to mine the inter-class similarity. The semi-supervised approach has a student–teacher network that pulls information from the teacher network and feeds it to the student network. The teacher network uses unlabeled data to form pseudo-boxes, and the student network uses both label data with the pseudo boxes and labeled data as the ground truth for training. It learns representations of furniture items by combining labeled and label data. On the Mask R-CNN detector with ResNet-101 backbone network, the proposed approach achieves a mAP of 98.8%, 99.7%, and 99.8% with only 1%, 5% and 10% labeled data, respectively. Our experiment affirms the efficiency of the proposed approach, as it outperforms the previous semi-supervised approaches using only 1% of the labels.
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