ISP-Teacher:Image Signal Process with Disentanglement Regularization for Unsupervised Domain Adaptive Dark Object Detection Article Swipe
Yin Zhang
,
Yongqiang Zhang
,
Zian Zhang
,
Man Zhang
,
Rui Tian
,
Mingli Ding
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i7.28569
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i7.28569
Object detection in dark conditions has always been a great challenge due to the complex formation process of low-light images. Currently, the mainstream methods usually adopt domain adaptation with Teacher-Student architecture to solve the dark object detection problem, and they imitate the dark conditions by using non-learnable data augmentation strategies on the annotated source daytime images. Note that these methods neglected to model the intrinsic imaging process, i.e. image signal processing (ISP), which is important for camera sensors to generate low-light images. To solve the above problems, in this paper, we propose a novel method named ISP-Teacher for dark object detection by exploring Teacher-Student architecture from a new perspective (i.e. self-supervised learning based ISP degradation). Specifically, we first design a day-to-night transformation module that consistent with the ISP pipeline of the camera sensors (ISP-DTM) to make the augmented images look more in line with the natural low-light images captured by cameras, and the ISP-related parameters are learned in a self-supervised manner. Moreover, to avoid the conflict between the ISP degradation and detection tasks in a shared encoder, we propose a disentanglement regularization (DR) that minimizes the absolute value of cosine similarity to disentangle two tasks and push two gradients vectors as orthogonal as possible. Extensive experiments conducted on two benchmarks show the effectiveness of our method in dark object detection. In particular, ISP-Teacher achieves an improvement of +2.4% AP and +3.3% AP over the SOTA method on BDD100k and SHIFT datasets, respectively. The code can be found at https://github.com/zhangyin1996/ISP-Teacher.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v38i7.28569
- https://ojs.aaai.org/index.php/AAAI/article/download/28569/29106
- OA Status
- diamond
- Cited By
- 8
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393147979
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https://openalex.org/W4393147979Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v38i7.28569Digital Object Identifier
- Title
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ISP-Teacher:Image Signal Process with Disentanglement Regularization for Unsupervised Domain Adaptive Dark Object DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-24Full publication date if available
- Authors
-
Yin Zhang, Yongqiang Zhang, Zian Zhang, Man Zhang, Rui Tian, Mingli DingList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v38i7.28569Publisher landing page
- PDF URL
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https://ojs.aaai.org/index.php/AAAI/article/download/28569/29106Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/28569/29106Direct OA link when available
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Regularization (linguistics), Artificial intelligence, Computer science, Image (mathematics), SIGNAL (programming language), Pattern recognition (psychology), Process (computing), Computer vision, Operating system, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6756505186, https://openalex.org/W6778485988, https://openalex.org/W4299418106, https://openalex.org/W3204374989, https://openalex.org/W4300079331, https://openalex.org/W2069281566, https://openalex.org/W3006800054, https://openalex.org/W3002804594, https://openalex.org/W6687483927, https://openalex.org/W4229078651, https://openalex.org/W6855765085, https://openalex.org/W6845748777, https://openalex.org/W4226042837, https://openalex.org/W6789505266, https://openalex.org/W6848226584, https://openalex.org/W2613718673, https://openalex.org/W4283066310, https://openalex.org/W4297631583, https://openalex.org/W4366198218, https://openalex.org/W3199451736, https://openalex.org/W6771876938, https://openalex.org/W2953106684, https://openalex.org/W4293584584, https://openalex.org/W4386065632, https://openalex.org/W2901996700, https://openalex.org/W4385483056, https://openalex.org/W3106539628, https://openalex.org/W4320350643, https://openalex.org/W2194775991, https://openalex.org/W4312458399, https://openalex.org/W3021542222, https://openalex.org/W3035731588, https://openalex.org/W4313160574, https://openalex.org/W2799352588, https://openalex.org/W3130976481, https://openalex.org/W2996490626, https://openalex.org/W4312993742, https://openalex.org/W4386075772, https://openalex.org/W3166409449, https://openalex.org/W4313030842, https://openalex.org/W3030520226, https://openalex.org/W4386075513 |
| referenced_works_count | 42 |
| abstract_inverted_index.a | 8, 92, 106, 119, 158, 174, 179 |
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| abstract_inverted_index.In | 220 |
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| abstract_inverted_index.an | 224 |
| abstract_inverted_index.as | 200, 202 |
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| abstract_inverted_index.be | 245 |
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| abstract_inverted_index.we | 90, 116, 177 |
| abstract_inverted_index.ISP | 113, 127, 168 |
| abstract_inverted_index.The | 242 |
| abstract_inverted_index.and | 38, 151, 170, 195, 229, 238 |
| abstract_inverted_index.are | 155 |
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| abstract_inverted_index.(i.e. | 109 |
| abstract_inverted_index.+2.4% | 227 |
| abstract_inverted_index.+3.3% | 230 |
| abstract_inverted_index.SHIFT | 239 |
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| abstract_inverted_index.using | 45 |
| abstract_inverted_index.value | 187 |
| abstract_inverted_index.which | 72 |
| abstract_inverted_index.(ISP), | 71 |
| abstract_inverted_index.Object | 0 |
| abstract_inverted_index.always | 6 |
| abstract_inverted_index.camera | 76, 131 |
| abstract_inverted_index.cosine | 189 |
| abstract_inverted_index.design | 118 |
| abstract_inverted_index.domain | 26 |
| abstract_inverted_index.images | 138, 147 |
| abstract_inverted_index.method | 94, 215, 235 |
| abstract_inverted_index.module | 122 |
| abstract_inverted_index.object | 35, 99, 218 |
| abstract_inverted_index.paper, | 89 |
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| abstract_inverted_index.signal | 69 |
| abstract_inverted_index.source | 53 |
| abstract_inverted_index.BDD100k | 237 |
| abstract_inverted_index.between | 166 |
| abstract_inverted_index.complex | 14 |
| abstract_inverted_index.daytime | 54 |
| abstract_inverted_index.images. | 19, 55, 81 |
| abstract_inverted_index.imaging | 65 |
| abstract_inverted_index.imitate | 40 |
| abstract_inverted_index.learned | 156 |
| abstract_inverted_index.manner. | 160 |
| abstract_inverted_index.methods | 23, 59 |
| abstract_inverted_index.natural | 145 |
| abstract_inverted_index.process | 16 |
| abstract_inverted_index.propose | 91, 178 |
| abstract_inverted_index.sensors | 77, 132 |
| abstract_inverted_index.usually | 24 |
| abstract_inverted_index.vectors | 199 |
| abstract_inverted_index.absolute | 186 |
| abstract_inverted_index.achieves | 223 |
| abstract_inverted_index.cameras, | 150 |
| abstract_inverted_index.captured | 148 |
| abstract_inverted_index.conflict | 165 |
| abstract_inverted_index.encoder, | 176 |
| abstract_inverted_index.generate | 79 |
| abstract_inverted_index.learning | 111 |
| abstract_inverted_index.pipeline | 128 |
| abstract_inverted_index.problem, | 37 |
| abstract_inverted_index.process, | 66 |
| abstract_inverted_index.(ISP-DTM) | 133 |
| abstract_inverted_index.Extensive | 204 |
| abstract_inverted_index.Moreover, | 161 |
| abstract_inverted_index.annotated | 52 |
| abstract_inverted_index.augmented | 137 |
| abstract_inverted_index.challenge | 10 |
| abstract_inverted_index.conducted | 206 |
| abstract_inverted_index.datasets, | 240 |
| abstract_inverted_index.detection | 1, 36, 100, 171 |
| abstract_inverted_index.exploring | 102 |
| abstract_inverted_index.formation | 15 |
| abstract_inverted_index.gradients | 198 |
| abstract_inverted_index.important | 74 |
| abstract_inverted_index.intrinsic | 64 |
| abstract_inverted_index.low-light | 18, 80, 146 |
| abstract_inverted_index.minimizes | 184 |
| abstract_inverted_index.neglected | 60 |
| abstract_inverted_index.possible. | 203 |
| abstract_inverted_index.problems, | 86 |
| abstract_inverted_index.Currently, | 20 |
| abstract_inverted_index.adaptation | 27 |
| abstract_inverted_index.benchmarks | 209 |
| abstract_inverted_index.conditions | 4, 43 |
| abstract_inverted_index.consistent | 124 |
| abstract_inverted_index.detection. | 219 |
| abstract_inverted_index.mainstream | 22 |
| abstract_inverted_index.orthogonal | 201 |
| abstract_inverted_index.parameters | 154 |
| abstract_inverted_index.processing | 70 |
| abstract_inverted_index.similarity | 190 |
| abstract_inverted_index.strategies | 49 |
| abstract_inverted_index.ISP-Teacher | 96, 222 |
| abstract_inverted_index.ISP-related | 153 |
| abstract_inverted_index.degradation | 169 |
| abstract_inverted_index.disentangle | 192 |
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| abstract_inverted_index.improvement | 225 |
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| abstract_inverted_index.Specifically, | 115 |
| abstract_inverted_index.degradation). | 114 |
| abstract_inverted_index.effectiveness | 212 |
| abstract_inverted_index.non-learnable | 46 |
| abstract_inverted_index.respectively. | 241 |
| abstract_inverted_index.regularization | 181 |
| abstract_inverted_index.transformation | 121 |
| abstract_inverted_index.Teacher-Student | 29, 103 |
| abstract_inverted_index.disentanglement | 180 |
| abstract_inverted_index.self-supervised | 110, 159 |
| abstract_inverted_index.https://github.com/zhangyin1996/ISP-Teacher. | 248 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.96784566 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |