Semi-Supervised Video Deraining with Dynamical Rain Generator Article Swipe
Zongsheng Yue
,
Jianwen Xie
,
Qian Zhao
,
Deyu Meng
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.07939
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.07939
While deep learning (DL)-based video deraining methods have achieved significant success recently, they still exist two major drawbacks. Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos. In fact, the rain layers exhibit strong physical properties (e.g., direction, scale and thickness) in spatial dimension and natural continuities in temporal dimension, and thus can be generally modelled by the spatial-temporal process in statistics. Secondly, current DL-based methods seriously depend on the labeled synthetic training data, whose rain types are always deviated from those in unlabeled real data. Such gap between synthetic and real data sets leads to poor performance when applying them in real scenarios. Against these issues, this paper proposes a new semi-supervised video deraining method, in which a dynamic rain generator is employed to fit the rain layer, expecting to better depict its insightful characteristics. Specifically, such dynamic generator consists of one emission model and one transition model to simultaneously encode the spatially physical structure and temporally continuous changes of rain streaks, respectively, which both are parameterized as deep neural networks (DNNs). Further more, different prior formats are designed for the labeled synthetic and unlabeled real data, so as to fully exploit the common knowledge underlying them. Last but not least, we also design a Monte Carlo EM algorithm to solve this model. Extensive experiments are conducted to verify the superiorities of the proposed semi-supervised deraining model.
Related Topics
Concepts
Generator (circuit theory)
Computer science
Parameterized complexity
Dimension (graph theory)
Exploit
Artificial intelligence
Pattern recognition (psychology)
Artificial neural network
Process (computing)
Data mining
Machine learning
Algorithm
Power (physics)
Mathematics
Operating system
Physics
Quantum mechanics
Pure mathematics
Computer security
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.07939
- https://arxiv.org/pdf/2103.07939
- OA Status
- green
- Cited By
- 4
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3138059268
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3138059268Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2103.07939Digital Object Identifier
- Title
-
Semi-Supervised Video Deraining with Dynamical Rain GeneratorWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-14Full publication date if available
- Authors
-
Zongsheng Yue, Jianwen Xie, Qian Zhao, Deyu MengList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.07939Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2103.07939Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2103.07939Direct OA link when available
- Concepts
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Generator (circuit theory), Computer science, Parameterized complexity, Dimension (graph theory), Exploit, Artificial intelligence, Pattern recognition (psychology), Artificial neural network, Process (computing), Data mining, Machine learning, Algorithm, Power (physics), Mathematics, Operating system, Physics, Quantum mechanics, Pure mathematics, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 48, 54, 67, 89, 108, 123 |
| abstract_inverted_index.is | 129 |
| abstract_inverted_index.of | 20, 28, 31, 148, 167, 229 |
| abstract_inverted_index.on | 75 |
| abstract_inverted_index.so | 195 |
| abstract_inverted_index.to | 102, 131, 137, 156, 197, 217, 225 |
| abstract_inverted_index.we | 209 |
| abstract_inverted_index.and | 46, 51, 57, 97, 152, 163, 191 |
| abstract_inverted_index.are | 84, 173, 185, 223 |
| abstract_inverted_index.but | 206 |
| abstract_inverted_index.can | 59 |
| abstract_inverted_index.fit | 132 |
| abstract_inverted_index.for | 187 |
| abstract_inverted_index.gap | 94 |
| abstract_inverted_index.its | 140 |
| abstract_inverted_index.new | 118 |
| abstract_inverted_index.not | 23, 207 |
| abstract_inverted_index.one | 149, 153 |
| abstract_inverted_index.the | 26, 36, 64, 76, 133, 159, 188, 200, 227, 230 |
| abstract_inverted_index.two | 15 |
| abstract_inverted_index.Last | 205 |
| abstract_inverted_index.Such | 93 |
| abstract_inverted_index.also | 210 |
| abstract_inverted_index.both | 172 |
| abstract_inverted_index.data | 99 |
| abstract_inverted_index.deep | 1, 176 |
| abstract_inverted_index.from | 87 |
| abstract_inverted_index.have | 7 |
| abstract_inverted_index.most | 19 |
| abstract_inverted_index.poor | 103 |
| abstract_inverted_index.rain | 29, 37, 82, 127, 134, 168 |
| abstract_inverted_index.real | 91, 98, 109, 193 |
| abstract_inverted_index.sets | 100 |
| abstract_inverted_index.such | 144 |
| abstract_inverted_index.them | 21, 107 |
| abstract_inverted_index.they | 12 |
| abstract_inverted_index.this | 114, 219 |
| abstract_inverted_index.thus | 58 |
| abstract_inverted_index.when | 105 |
| abstract_inverted_index.Carlo | 214 |
| abstract_inverted_index.Monte | 213 |
| abstract_inverted_index.While | 0 |
| abstract_inverted_index.data, | 80, 194 |
| abstract_inverted_index.data. | 92 |
| abstract_inverted_index.exist | 14 |
| abstract_inverted_index.fact, | 35 |
| abstract_inverted_index.fully | 198 |
| abstract_inverted_index.leads | 101 |
| abstract_inverted_index.major | 16 |
| abstract_inverted_index.model | 25, 151, 155 |
| abstract_inverted_index.more, | 181 |
| abstract_inverted_index.paper | 115 |
| abstract_inverted_index.prior | 183 |
| abstract_inverted_index.rainy | 32 |
| abstract_inverted_index.scale | 45 |
| abstract_inverted_index.solve | 218 |
| abstract_inverted_index.still | 13 |
| abstract_inverted_index.them. | 204 |
| abstract_inverted_index.these | 112 |
| abstract_inverted_index.those | 88 |
| abstract_inverted_index.types | 83 |
| abstract_inverted_index.video | 4, 120 |
| abstract_inverted_index.which | 124, 171 |
| abstract_inverted_index.whose | 81 |
| abstract_inverted_index.(e.g., | 43 |
| abstract_inverted_index.always | 85 |
| abstract_inverted_index.better | 138 |
| abstract_inverted_index.common | 201 |
| abstract_inverted_index.depend | 74 |
| abstract_inverted_index.depict | 139 |
| abstract_inverted_index.design | 211 |
| abstract_inverted_index.encode | 158 |
| abstract_inverted_index.layer, | 135 |
| abstract_inverted_index.layers | 30, 38 |
| abstract_inverted_index.least, | 208 |
| abstract_inverted_index.model. | 220, 234 |
| abstract_inverted_index.neural | 177 |
| abstract_inverted_index.strong | 40 |
| abstract_inverted_index.verify | 226 |
| abstract_inverted_index.(DNNs). | 179 |
| abstract_inverted_index.Against | 111 |
| abstract_inverted_index.Further | 180 |
| abstract_inverted_index.between | 95 |
| abstract_inverted_index.changes | 166 |
| abstract_inverted_index.current | 70 |
| abstract_inverted_index.dynamic | 126, 145 |
| abstract_inverted_index.exhibit | 39 |
| abstract_inverted_index.exploit | 199 |
| abstract_inverted_index.formats | 184 |
| abstract_inverted_index.issues, | 113 |
| abstract_inverted_index.labeled | 77, 189 |
| abstract_inverted_index.method, | 122 |
| abstract_inverted_index.methods | 6, 72 |
| abstract_inverted_index.natural | 52 |
| abstract_inverted_index.process | 66 |
| abstract_inverted_index.spatial | 49 |
| abstract_inverted_index.success | 10 |
| abstract_inverted_index.videos. | 33 |
| abstract_inverted_index.DL-based | 71 |
| abstract_inverted_index.Firstly, | 18 |
| abstract_inverted_index.achieved | 8 |
| abstract_inverted_index.applying | 106 |
| abstract_inverted_index.consists | 147 |
| abstract_inverted_index.designed | 186 |
| abstract_inverted_index.deviated | 86 |
| abstract_inverted_index.emission | 150 |
| abstract_inverted_index.employed | 130 |
| abstract_inverted_index.learning | 2 |
| abstract_inverted_index.modelled | 62 |
| abstract_inverted_index.networks | 178 |
| abstract_inverted_index.physical | 41, 161 |
| abstract_inverted_index.proposed | 231 |
| abstract_inverted_index.proposes | 116 |
| abstract_inverted_index.streaks, | 169 |
| abstract_inverted_index.temporal | 55 |
| abstract_inverted_index.training | 79 |
| abstract_inverted_index.Extensive | 221 |
| abstract_inverted_index.Secondly, | 69 |
| abstract_inverted_index.algorithm | 216 |
| abstract_inverted_index.conducted | 224 |
| abstract_inverted_index.deraining | 5, 121, 233 |
| abstract_inverted_index.different | 182 |
| abstract_inverted_index.dimension | 50 |
| abstract_inverted_index.expecting | 136 |
| abstract_inverted_index.generally | 61 |
| abstract_inverted_index.generator | 128, 146 |
| abstract_inverted_index.knowledge | 202 |
| abstract_inverted_index.recently, | 11 |
| abstract_inverted_index.seriously | 73 |
| abstract_inverted_index.spatially | 160 |
| abstract_inverted_index.structure | 162 |
| abstract_inverted_index.synthetic | 78, 96, 190 |
| abstract_inverted_index.unlabeled | 90, 192 |
| abstract_inverted_index.(DL)-based | 3 |
| abstract_inverted_index.continuous | 165 |
| abstract_inverted_index.dimension, | 56 |
| abstract_inverted_index.direction, | 44 |
| abstract_inverted_index.drawbacks. | 17 |
| abstract_inverted_index.insightful | 141 |
| abstract_inverted_index.properties | 42 |
| abstract_inverted_index.scenarios. | 110 |
| abstract_inverted_index.temporally | 164 |
| abstract_inverted_index.thickness) | 47 |
| abstract_inverted_index.transition | 154 |
| abstract_inverted_index.underlying | 203 |
| abstract_inverted_index.experiments | 222 |
| abstract_inverted_index.performance | 104 |
| abstract_inverted_index.significant | 9 |
| abstract_inverted_index.statistics. | 68 |
| abstract_inverted_index.continuities | 53 |
| abstract_inverted_index.sufficiently | 24 |
| abstract_inverted_index.Specifically, | 143 |
| abstract_inverted_index.parameterized | 174 |
| abstract_inverted_index.respectively, | 170 |
| abstract_inverted_index.superiorities | 228 |
| abstract_inverted_index.simultaneously | 157 |
| abstract_inverted_index.characteristics | 27 |
| abstract_inverted_index.semi-supervised | 119, 232 |
| abstract_inverted_index.characteristics. | 142 |
| abstract_inverted_index.spatial-temporal | 65 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |