Informative Data Selection with Uncertainty for Multi-modal Object Detection Article Swipe
Xinyu Zhang
,
Zhiwei Li
,
Zhenhong Zou
,
Xin Gao
,
Yijin Xiong
,
Dafeng Jin
,
Jun Li
,
Huaping Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.11697
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.11697
Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurate recognition due to the shift in the observed pattern, that requires a robust generalization of the models. To implement a general vision model, we need to develop deep learning models that can adaptively select valid information from multi-modal data. This is mainly based on two reasons. Multi-modal learning can break through the inherent defects of single-modal data, and adaptive information selection can reduce chaos in multi-modal data. To tackle this problem, we propose a universal uncertainty-aware multi-modal fusion model. It adopts a multi-pipeline loosely coupled architecture to combine the features and results from point clouds and images. To quantify the correlation in multi-modal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation. In this way, our model reduces the randomness in fusion and generates reliable output. Moreover, we conducted a completed investigation on the KITTI 2D object detection dataset and its derived dirty data. Our fusion model is proven to resist severe noise interference like Gaussian, motion blur, and frost, with only slight degradation. The experiment results demonstrate the benefits of our adaptive fusion. Our analysis on the robustness of multi-modal fusion will provide further insights for future research.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.11697
- https://arxiv.org/pdf/2304.11697
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366999814
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4366999814Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2304.11697Digital Object Identifier
- Title
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Informative Data Selection with Uncertainty for Multi-modal Object DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-23Full publication date if available
- Authors
-
Xinyu Zhang, Zhiwei Li, Zhenhong Zou, Xin Gao, Yijin Xiong, Dafeng Jin, Jun Li, Huaping LiuList of authors in order
- Landing page
-
https://arxiv.org/abs/2304.11697Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2304.11697Direct 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/2304.11697Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Modal, Robustness (evolution), Sensor fusion, Machine learning, Data mining, Computer vision, Gene, Polymer chemistry, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.problem, | 97 |
| abstract_inverted_index.quantify | 125 |
| abstract_inverted_index.reasons. | 72 |
| abstract_inverted_index.reducing | 16 |
| abstract_inverted_index.reliable | 164 |
| abstract_inverted_index.requires | 37 |
| abstract_inverted_index.Gaussian, | 195 |
| abstract_inverted_index.Moreover, | 166 |
| abstract_inverted_index.completed | 170 |
| abstract_inverted_index.conducted | 168 |
| abstract_inverted_index.confusion | 11 |
| abstract_inverted_index.detection | 8, 177 |
| abstract_inverted_index.different | 142 |
| abstract_inverted_index.generates | 163 |
| abstract_inverted_index.implement | 45 |
| abstract_inverted_index.research. | 228 |
| abstract_inverted_index.selection | 87 |
| abstract_inverted_index.universal | 101 |
| abstract_inverted_index.adaptively | 59 |
| abstract_inverted_index.experiment | 205 |
| abstract_inverted_index.inaccurate | 26 |
| abstract_inverted_index.modalities | 143 |
| abstract_inverted_index.randomness | 159 |
| abstract_inverted_index.reasoning, | 14 |
| abstract_inverted_index.robustness | 218 |
| abstract_inverted_index.Multi-modal | 73 |
| abstract_inverted_index.correlation | 127 |
| abstract_inverted_index.demonstrate | 207 |
| abstract_inverted_index.generation. | 151 |
| abstract_inverted_index.information | 62, 86 |
| abstract_inverted_index.multi-modal | 64, 92, 103, 129, 220 |
| abstract_inverted_index.recognition | 27 |
| abstract_inverted_index.architecture | 112 |
| abstract_inverted_index.degradation. | 203 |
| abstract_inverted_index.information, | 130, 140 |
| abstract_inverted_index.interference | 193 |
| abstract_inverted_index.single-modal | 82 |
| abstract_inverted_index.uncertainty, | 134 |
| abstract_inverted_index.investigation | 171 |
| abstract_inverted_index.nonnegligible | 4 |
| abstract_inverted_index.generalization | 40 |
| abstract_inverted_index.multi-pipeline | 109 |
| abstract_inverted_index.informativeness | 18 |
| abstract_inverted_index.uncertainty-aware | 102 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |