Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement | |
Zhang, Chi1; Lin, Zihang1; Xu, Liheng1 ; Li, Zongliang1; Tang, Wei2; Liu, Yuehu1; Meng, Gaofeng3,4,5; Wang, Le1; Li, Li6
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| Source Publication | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| ISSN | 1051-8215 |
| 2022-07-01 | |
| Volume | 32Issue:7Pages:4552-4572 |
| Corresponding Author | Liu, Yuehu(liuyh@xjtu.edu.cn) |
| Abstract | The key procedure of haze image synthesis with adversarial training lies in the disentanglement of the feature involved only in haze synthesis, i.e., the style feature, from the feature representing the invariant semantic content, i.e., the content feature. Previous methods introduced a binary classifier to constrain the domain membership from being distinguished through the learned content feature during the training stage, thereby the style information is separated from the content feature. However, we find that these methods cannot achieve complete content-style disentanglement. The entanglement of the flawed style feature with content information inevitably leads to the inferior rendering of haze images. To address this issue, we propose a self-supervised style regression model with stochastic linear interpolation that can suppress the content information in the style feature. Ablative experiments demonstrate the disentangling completeness and its superiority in density-aware haze image synthesis. Moreover, the synthesized haze data are applied to test the generalization ability of vehicle detectors. Further study on the relation between haze density and detection performance shows that haze has an obvious impact on the generalization ability of vehicle detectors and that the degree of performance degradation is linearly correlated to the haze density, which in turn validates the effectiveness of the proposed method. |
| Keyword | Feature extraction Image synthesis Scattering Generative adversarial networks Atmospheric modeling Training Testing Haze synthesis unsupervised image-to-image translation self-supervised disentanglement |
| DOI | 10.1109/TCSVT.2021.3130158 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key Research and Development Project of New Generation Artificial Intelligence of China[2018AAA0102504] ; National Natural Science Foundation of China[61973245] |
| Funding Organization | National Key Research and Development Project of New Generation Artificial Intelligence of China ; National Natural Science Foundation of China |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Electrical & Electronic |
| WOS ID | WOS:000819817700037 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/49195 |
| Collection | 模式识别国家重点实验室_先进时空数据分析与学习 |
| Corresponding Author | Liu, Yuehu |
| Affiliation | 1.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Shaanxi, Peoples R China 2.Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA 3.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Ctr Artificial Intelligence & Robot, HK Inst Sci & Innovat, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 6.Tsinghua Univ, Dept Automat, BNRist, Beijing 100084, Peoples R China |
| Recommended Citation GB/T 7714 | Zhang, Chi,Lin, Zihang,Xu, Liheng,et al. Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(7):4552-4572. |
| APA | Zhang, Chi.,Lin, Zihang.,Xu, Liheng.,Li, Zongliang.,Tang, Wei.,...&Li, Li.(2022).Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(7),4552-4572. |
| MLA | Zhang, Chi,et al."Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.7(2022):4552-4572. |
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