Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
| Subtitle of host publication | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
| Place of Publication | Nashville, TN, USA |
| Pages | 1663-1671 |
| Number of pages | 9 |
| ISBN (electronic) | 978-1-6654-4899-4 |
| Publication status | Published - 2021 |
| Event | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States Duration: 19 Jun 2021 → 25 Jun 2021 |
Publication series
| Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
|---|---|
| ISSN (Print) | 2160-7508 |
| ISSN (electronic) | 2160-7516 |
Abstract
Keywords
- cs.CV
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
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Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Nashville, TN, USA , 2021. p. 1663-1671 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Target-Tailored Source-Transformation for Scene Graph Generation
AU - Liao, Wentong
AU - Lan, Cuiling
AU - Zeng, Wenjun
AU - Yang, Michael Ying
AU - Rosenhahn, Bodo
N1 - Funding Information: This work was supported by the Center for Digital Innovations (ZDIN), Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILa-bor(grant no.01DD20003) and the Deutsche Forschungs-gemeinschaft (DFG) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122).
PY - 2021
Y1 - 2021
N2 - Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performing works to date are based on exploiting the context surrounding objects or relations,e.g., by passing information among objects. In these approaches, to transform the representation of source objects is a critical process for extracting information for the use by target objects. In this work, we argue that a source object should give what tar-get object needs and give different objects different information rather than contributing common information to all targets. To achieve this goal, we propose a Target-TailoredSource-Transformation (TTST) method to efficiently propagate information among object proposals and relations. Particularly, for a source object proposal which will contribute information to other target objects, we transform the source object feature to the target object feature domain by simultaneously taking both the source and target into account. We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation. By doing so the target object is able to extract target-specific information from the source object and source relation accordingly to refine its representation. Our framework is validated on the Visual Genome bench-mark and demonstrated its state-of-the-art performance for the scene graph generation. The experimental results show that the performance of object detection and visual relation-ship detection are promoted mutually by our method.
AB - Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performing works to date are based on exploiting the context surrounding objects or relations,e.g., by passing information among objects. In these approaches, to transform the representation of source objects is a critical process for extracting information for the use by target objects. In this work, we argue that a source object should give what tar-get object needs and give different objects different information rather than contributing common information to all targets. To achieve this goal, we propose a Target-TailoredSource-Transformation (TTST) method to efficiently propagate information among object proposals and relations. Particularly, for a source object proposal which will contribute information to other target objects, we transform the source object feature to the target object feature domain by simultaneously taking both the source and target into account. We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation. By doing so the target object is able to extract target-specific information from the source object and source relation accordingly to refine its representation. Our framework is validated on the Visual Genome bench-mark and demonstrated its state-of-the-art performance for the scene graph generation. The experimental results show that the performance of object detection and visual relation-ship detection are promoted mutually by our method.
KW - cs.CV
UR - http://www.scopus.com/inward/record.url?scp=85115193370&partnerID=8YFLogxK
U2 - 10.1109/CVPRW53098.2021.00182
DO - 10.1109/CVPRW53098.2021.00182
M3 - Conference contribution
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1663
EP - 1671
BT - Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
CY - Nashville, TN, USA
T2 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Y2 - 19 June 2021 through 25 June 2021
ER -