基于渐进多源域迁移的无监督跨域目标检测

李威; 王蒙

doi:10.16383/j.aas.c190532

基于渐进多源域迁移的无监督跨域目标检测

doi: 10.16383/j.aas.c190532

李威^{1, 2,},
王蒙^{1, 2,}

1.
昆明理工大学信息工程与自动化学院昆明 650500
2.
昆明理工大学云南省人工智能重点实验室昆明 650500

基金项目: 国家自然科学基金(61563025)和云南省科技计划项目(2016FB-109)资助

详细信息

作者简介:
李威：昆明理工大学信息工程与自动化学院硕士研究生. 主要研究方向为图像处理, 计算机视觉以及模式识别. E-mail: leesoon2049@gmail.com

王蒙：博士, 昆明理工大学信息工程与自动化学院副教授. 主要研究方向为图像处理, 计算机视觉以及模式识别. 本文通信作者.E-mail: wmeng06@126.com

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出版历程
- 收稿日期: 2019-10-25
- 录用日期: 2020-03-11
- 网络出版日期: 2022-07-06
- 刊出日期: 2022-09-16

Unsupervised Cross-domain Object Detection Based on Progressive Multi-source Transfer

LI Wei^{1, 2
,},
WANG Meng^{1, 2
,}

1.
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500
2.
Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500

Funds: Supported by National Natural Science Foundation of China (61563025) and Yunnan Science and Technology Department of Science and Technology Project (2016FB109)

More Information

Author Bio:
LI Wei　Master student at the School of Information Engineering and Automation, Kunming University of Science and Technology. His research interest covers image processing, computer vision and pattern recognition

WANG Meng　Ph.D., associate professor at the School of Information Engineering and Automation, Kunming University of Science and Technology. His research interest covers image processing, computer vision and pattern recognition. Corresponding author of this paper

摘要

摘要: 针对目标检测任务中获取人工标注训练样本的困难, 提出一种在像素级与特征级渐进完成域自适应的无监督跨域目标检测方法. 现有的像素级域自适应方法中, 存在翻译图像风格单一、内容结构不一致的问题. 因此, 将输入图像分解为域不变的内容空间及域特有的属性空间, 综合不同空间表示进行多样性的图像翻译, 同时保留图像的空间语义结构以实现标注信息的迁移. 此外, 对特征级域自适应而言, 为缓解单源域引起的源域偏向问题, 将得到的带有标注的多样性翻译图像作为多源域训练集, 设计基于多领域的对抗判别模块, 从而获取多个领域不变的特征表示. 最后, 采用自训练方案迭代生成目标域训练集伪标签, 以进一步提升模型在目标域上的检测效果. 在Cityscapes & Foggy Cityscapes与VOC07 & Clipart1k数据集上的实验结果表明, 相比现有的无监督跨域检测算法, 该检测框架具更优越的迁移检测性能.
- 迁移学习 /
- 域自适应 /
- 目标检测 /
- 多源域 /
- 自训练
Abstract: To address the difficulty of collecting manually labeled training samples for object detection tasks, this paper proposes an unsupervised cross-domain object detection method that gradually adapts the model at pixel level and feature level. The existing pixel-level domain adaptive methods generate translated images with a single style and inconsistent content structure. To solve this problem, this paper embeds the input images into domain-invariant content space and domain-specific attribute space, then cooperates different space representations to synthesize diverse translated images that preserve the spatial semantic information to enable label transfer. In addition, for feature-level domain adaptation, to alleviate the source-bias problem caused by single source domain, we treat the generated diverse labeled images as source domain data and design a multi-domain discriminator to get multi-domain-invariant representations. Finally, To further enhance the detection performance on the target domain, we propose a self-training framework to alternatively generate pseudo labels on target training data. The exploratory experiment results from the Cityscapes & Foggy Cityscapes dataset and VOC07 & Clipart1k dataset demonstrate that compared with the current unsupervised cross-domain detection methods, the proposed detection framework achieves better transferability.
- Transfer learning /
- domain adaptation /
- object detection /
- multi-source domain /
- self training

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图 1 Cityscapes^[9] (上)与Foggy Cityscapes^[10] (下)示例图

Fig. 1 Examples from Cityscapes^[9] (up) and Foggy Cityscapes^[10] (bottom)

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图 2 无监督跨域目标检测方法结构图

Fig. 2 Diagram for unsupervised cross-domain object detection

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图 3 损失函数

Fig. 3 Loss function

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图 4 分解表示所采用模块网络结构

Fig. 4 Modular network structures used in the disentangled representation framework

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图 5 图像翻译中采用的生成器与判别器网络结构

Fig. 5 Network structures of the generator and the discriminator used in image-to-image translation

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图 6 多域不变特征表示

Fig. 6 Multi-domain-invariant representation

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图 7 在Cityscapes$ \rightarrow $Foggy Cityscapes实验中不同方法在所有8个类别上的mAP表现

Fig. 7 Percategory mAP performance of different approaches over all the 8 categories on the experimentCityscapes$ \rightarrow $Foggy Cityscapes

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图 8 在VOC07$ \rightarrow $Clipart1k实验中不同方法在所有20个类别上的mAP表现

Fig. 8 Percategory mAP performance of differentapproaches over all the 20 categories on the experiment VOC07$ \rightarrow $Clipart1k

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图 9 多种方法在Cityscapes$ \rightarrow $Foggy Cityscapes实验中检测结果对比

Fig. 9 Comparison of different detection methods in the Cityscapes$ \rightarrow $Foggy Cityscapes experiment

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图 10 不同方法在VOC07$\rightarrow$Clipart1k实验中检测结果对比

Fig. 10 Comparison of different detection methods in the VOC07$ \rightarrow $Clipart1k experiment

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图 11 每一成分对mAP的提升

Fig. 11 The mAP gain of each component

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图 12 图像翻译结果示例图

Fig. 12 Sample results of translated images

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表 1 不同目标检测方法mAP性能对比 (%)

Table 1 Comparison of different detection methods on performance of mAP (%)

方法	迁移集 1	迁移集 2
基线方法 (SSD300)	17.4	23.2
DAAN^[33]	25.9	28.4
CycleGAN^[17]	27.9	30.0
DT^[17]	23.3	25.6
像素级对齐	29.5	31.8
多源特征对齐	32.7	36.2
自训练	20.1	23.9
多源特征对齐 + 自训练	32.9	38.6
全监督	33.0	48.4

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表 2 在 Cityscapes$ \rightarrow $Foggy Cityscapes 实验中基于Faster R-CNN 的不同跨域检测方法性能对比 (%)

Table 2 Comparison of different cross-domain detection methods based on Faster R-CNN detector in Cityscapes$ \rightarrow $Foggy Cityscapes (%)

方法	Person	Rider	Car	Truck	Bus	Train	Motorcycle	Bicycle	mAP
基线方法 (Faster R-CNN)	24.7	31.7	33.0	11.5	24.4	9.5	15.9	28.9	22.5
域自适应 Faster R-CNN^[19]	25.0	31.0	40.5	22.1	35.3	20.2	20.0	27.1	27.6
DT^[17]	25.4	39.3	42.4	24.9	40.4	23.1	25.9	30.4	31.5
选择性跨域对齐^[21]	33.5	38.0	48.5	26.5	39.0	23.3	28.0	33.6	33.8
多对抗超快速区域卷积网络^[23]	28.2	39.5	43.9	23.8	39.9	33.3	29.2	33.9	34.0
强弱分布对齐^[20]	29.9	42.3	43.5	24.5	36.2	32.6	30.0	35.3	34.3
域自适应表示学习^[18]	30.8	40.5	44.3	27.2	38.4	34.5	28.4	32.2	34.6
一致性教师客体关系^[22]	30.6	41.4	44.0	21.9	38.6	40.6	28.3	35.6	35.1
多层域自适应^[24]	33.2	44.2	44.8	28.2	41.8	28.7	30.5	36.5	36.0
加噪标签^[26]	35.1	42.1	49.2	30.1	45.3	26.9	26.8	36.0	36.5
像素级 + 多域特征对齐 (联合训练)	32.3	42.5	49.1	26.5	44.6	32.8	31.5	35.6	36.9
像素级对齐	33.1	43.0	49.4	28.0	43.3	35.2	35.4	36.3	38.0
多域特征对齐	33.0	43.8	48.5	26.7	45.2	44.6	30.8	37.0	38.7
像素级 + 多域特征对齐 + 自训练	35.7	45.6	51.7	31.0	47.0	41.4	30.3	36.7	39.9
全监督	35.4	47.1	52.4	29.6	42.7	46.3	33.8	38.4	40.7

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表 3 在Cityscapes$\rightarrow $Foggy Cityscapes实验中源域数量$M$对检测性能的影响 (%)

Table 3 Impact of the number of source domains $M$ on the detection performance in Cityscapes$\rightarrow $Foggy Cityscapes (%)

M	0	1	2	3
像素级对齐 mAP	17.4	27.3	28.9	29.5
多源域特征对齐 mAP	17.4	29.6	30.3	32.7

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表 4 Cityscapes$\rightarrow $Foggy Cityscapes实验中属性特征对检测性能的影响 (%)

Table 4 Impact of attribute features on the detection performance in Cityscapes$ \rightarrow$Foggy Cityscapes (%)

方法	框架	mAP
像素级对齐 (随机属性)	SSD	29.0
像素级对齐	SSD	29.5
多源域特征对齐 (随机属性)	SSD	31.6
多源域特征对齐	SSD	32.7
像素级对齐 (随机属性)	Faster R-CNN	34.7
像素级对齐	Faster R-CNN	38.0
多源域特征对齐 (随机属性)	Faster R-CNN	36.5
多源域特征对齐	Faster R-CNN	38.7

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表 5 在VOC07$ \rightarrow$Clipart1k实验中参数$ \lambda $的敏感性分析 (%)

Table 5 Sensitivity analysis of $ \lambda $ in VOC07$\rightarrow $Clipart1k (%)

$\lambda$	0.1	0.5	1.0	1.5	2.0
mAP	34.0	36.1	36.3	36.1	35.7

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表 6 在VOC07$ \rightarrow$Clipart1k实验中阈值$ \theta $的敏感性分析 (%)

Table 6 Sensitivity analysis of $ \theta $ in VOC07$\rightarrow $Clipart1k (%)

$ \theta $	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
第 1 轮 mAP	37.8	38.3	38.1	37.8	37.7	37.2	36.2	35.7	35.0
第 2 轮 mAP	−	−	38.0	38.3	38.4	38.6	38.6	38.3	38.4
第 3 轮 mAP	−	−	−	−	−	−	38.6	38.3	38.4

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