计算机科学
最小边界框
领域(数学分析)
跳跃式监视
域适应
人工智能
目标检测
对象(语法)
噪音(视频)
班级(哲学)
适应(眼睛)
透视图(图形)
计算机视觉
训练集
集合(抽象数据类型)
机器学习
视觉对象识别的认知神经科学
稳健性(进化)
数据建模
模式识别(心理学)
图像(数学)
数学
数据库
数学分析
物理
化学
程序设计语言
光学
生物化学
基因
分类器(UML)
作者
Mehran Khodabandeh,Arash Vahdat,Mohammad Ranjbar,William G. Macready
标识
DOI:10.1109/iccv.2019.00057
摘要
Domain shift is unavoidable in real-world applications of object detection. For example, in self-driving cars, the target domain consists of unconstrained road environments which cannot all possibly be observed in training data. Similarly, in surveillance applications sufficiently representative training data may be lacking due to privacy regulations. In this paper, we address the domain adaptation problem from the perspective of robust learning and show that the problem may be formulated as training with noisy labels. We propose a robust object detection framework that is resilient to noise in bounding box class labels, locations and size annotations. To adapt to the domain shift, the model is trained on the target domain using a set of noisy object bounding boxes that are obtained by a detection model trained only in the source domain. We evaluate the accuracy of our approach in various source/target domain pairs and demonstrate that the model significantly improves the state-of-the-art on multiple domain adaptation scenarios on the SIM10K, Cityscapes and KITTI datasets.
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