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|a 10.1109/TPAMI.2023.3235367
|2 doi
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|a pubmed24n1184.xml
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Li, Wuyang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a SIGMA++
|b Improved Semantic-Complete Graph Matching for Domain Adaptive Object Detection
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 06.06.2023
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|a Date Revised 06.06.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Domain Adaptive Object Detection (DAOD) generalizes the object detector from an annotated domain to a label-free novel one. Recent works estimate prototypes (class centers) and minimize the corresponding distances to adapt the cross-domain class conditional distribution. However, this prototype-based paradigm 1) fails to capture the class variance with agnostic structural dependencies, and 2) ignores the domain-mismatched classes with a sub-optimal adaptation. To address these two challenges, we propose an improved SemantIc-complete Graph MAtching framework, dubbed SIGMA++, for DAOD, completing mismatched semantics and reformulating adaptation with hypergraph matching. Specifically, we propose a Hypergraphical Semantic Completion (HSC) module to generate hallucination graph nodes in mismatched classes. HSC builds a cross-image hypergraph to model class conditional distribution with high-order dependencies and learns a graph-guided memory bank to generate missing semantics. After representing the source and target batch with hypergraphs, we reformulate domain adaptation with a hypergraph matching problem, i.e., discovering well-matched nodes with homogeneous semantics to reduce the domain gap, which is solved with a Bipartite Hypergraph Matching (BHM) module. Graph nodes are used to estimate semantic-aware affinity, while edges serve as high-order structural constraints in a structure-aware matching loss, achieving fine-grained adaptation with hypergraph matching. The applicability of various object detectors verifies the generalization of SIGMA++, and extensive experiments on nine benchmarks show its state-of-the-art performance on both AP 50 and adaptation gains
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Liu, Xinyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yuan, Yixuan
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 7 vom: 09. Juli, Seite 9022-9040
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:7
|g day:09
|g month:07
|g pages:9022-9040
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2023.3235367
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