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|a 10.1109/TPAMI.2019.2950025
|2 doi
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|a pubmed24n1009.xml
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|a eng
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| 100 |
1 |
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|a Liu, Daqi
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Visual Semantic Information Pursuit
|b A Survey
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| 264 |
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1 |
|c 2021
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 29.09.2021
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| 500 |
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|a Date Revised 29.09.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Visual semantic information comprises two important parts: the meaning of each visual semantic unit and the coherent visual semantic relation conveyed by these visual semantic units. Essentially, the former one is a visual perception task while the latter corresponds to visual context reasoning. Remarkable advances in visual perception have been achieved due to the success of deep learning. In contrast, visual semantic information pursuit, a visual scene semantic interpretation task combining visual perception and visual context reasoning, is still in its early stage. It is the core task of many different computer vision applications, such as object detection, visual semantic segmentation, visual relationship detection, or scene graph generation. Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual semantic information pursuit methods. Surprisingly, a comprehensive review for this exciting area is still lacking. In this survey, we present a unified theoretical paradigm for all these methods, followed by an overview of the major developments and the future trends in each potential direction. The common benchmark datasets, the evaluation metrics and the comparisons of the corresponding methods are also introduced
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Bober, Miroslaw
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kittler, Josef
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 43(2021), 4 vom: 29. Apr., Seite 1404-1422
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:43
|g year:2021
|g number:4
|g day:29
|g month:04
|g pages:1404-1422
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|u http://dx.doi.org/10.1109/TPAMI.2019.2950025
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