Modelling relations with prototypes for visual relation detection
Relations between objects drive our understanding of images. Modelling them poses several challenges due to the combinatorial nature of the problem and the complex structure of natural language. This paper tackles the task of predicting relationships in the form of (subject, relation, object) triple...
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| Published in | Multimedia tools and applications Vol. 80; no. 15; pp. 22465 - 22486 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.06.2021
Springer Nature B.V Springer Verlag |
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| Abstract | Relations between objects drive our understanding of images. Modelling them poses several challenges due to the combinatorial nature of the problem and the complex structure of natural language. This paper tackles the task of predicting relationships in the form of (subject, relation, object) triplets from still images. To address these issues, we propose a framework for learning relation prototypes that aims to capture the complex nature of relation distributions. Concurrently, a network is trained to define a space in which relationship triplets with similar spatial layouts, interacting objects and relations are clustered together. Finally, the network is compared to two models explicitly tackling the problem of synonymy among relations. For this, two well known scene-graph labelling benchmarks are used for training and testing: VRD and Visual Genome. Prediction of relations based on distance to prototype provides a significant increase in the diversity of predicted relations, improving the average relation recall from 40.3% to 41.7% on the first and 31.3% to 35.4% on the second. |
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| AbstractList | Relations between objects drive our understanding of images. Modelling them poses several challenges due to the combinatorial nature of the problem and the complex structure of natural language. This paper tackles the task of predicting relationships in the form of (subject, relation, object) triplets from still images. To address these issues, we propose a framework for learning relation prototypes that aims to capture the complex nature of relation distributions. Concurrently, a network is trained to define a space in which relationship triplets with similar spatial layouts, interacting objects and relations are clustered together. Finally, the network is compared to two models explicitly tackling the problem of synonymy among relations. For this, two well known scene-graph labelling benchmarks are used for training and testing: VRD and Visual Genome. Prediction of relations based on distance to prototype provides a significant increase in the diversity of predicted relations, improving the average relation recall from 40.3% to 41.7% on the first and 31.3% to 35.4% on the second. |
| Author | Prêteux, Françoise Plesse, François Ginsca, Alexandru Delezoide, Bertrand |
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| BackLink | https://cea.hal.science/cea-04575385$$DView record in HAL |
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| Cites_doi | 10.1109/CVPR.2018.00872 10.1109/IJCNN.2019.8851814 10.1007/978-3-030-01219-9_20 10.1109/ICCV.2015.122 10.1109/CVPR.2017.766 10.1139/h11-025 10.1109/WACV45572.2020.9093605 10.1109/CVPR.2018.00717 10.1109/CVPR.2017.352 10.1007/s11042-015-2757-4 10.1109/CVPR.2016.90 10.1109/CVPR.2017.10 10.24963/ijcai.2017/230 10.1109/CVPR.2018.00611 10.1109/ICME.2018.8486503 10.1109/CVPR.2015.7298682 10.1007/978-3-319-10590-1_4 10.1109/ICCV.2015.169 10.1609/aaai.v32i1.12274 10.18653/v1/P16-1228 10.1109/CVPR.2017.469 10.1109/ICCV.2017.554 10.1109/CVPR.2017.653 10.1007/978-3-319-46448-0_51 10.1016/j.infsof.2008.09.005 10.1109/ICME.2017.8019448 10.1109/ICCV.2017.121 10.1109/CVPR.2015.7299054 10.1162/153244303322533223 10.1007/s11263-016-0981-7 10.1109/CVPR.2017.330 10.1109/CVPR.2016.130 10.1109/CVPR.2016.91 |
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| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020. Distributed under a Creative Commons Attribution 4.0 International License |
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| Issue | 15 |
| Keywords | Visual relation detection Synonyms Prototype Nearest neighbors Metric learning |
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| References_xml | – reference: Newell A, Deng J (2017) Pixels to graphs by associative embedding. In: NIPS – reference: Long Y, Liu L, Shao L, Shen F, Ding G, Han J (2017) From zero-shot learning to conventional supervised classification: Unseen visual data synthesis. In: CVPR. https://doi.org/10.1109/CVPR.2017.653 – reference: Johnson J, Douze M, Jégou H Billion-scale similarity search with GPUs – reference: Zellers R, Yatskar M, Thomson S, Choi Y (2018) Neural motifs: Scene graph parsing with global context. In: CVPR – reference: Schroff F, Philbin J (2015) Facenet: A Unified Embedding for Face Recognition and Clustering. In: CVPR – reference: Gkioxari G, Girshick R, Dollár P, He K (2018) Detecting and recognizing Human-Object interactions. In: CVPR – reference: Cui Y, Zhou F, Lin Y, Belongie S (2016) Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop. In: CVPR – reference: Deng J, Ding N, Jia Y, Frome A, Murphy K, Bengio S, Li Y, Neven H, Adam H (2014) Large-Scale Object classification using label relation graphs. In: European conference on computer vision – reference: Macqueen J (1967) Some methods for classification and analysis of multivariate observations. In: Fifth berkeley symposium on mathematical statistics and probability – reference: Vinyals O, Deepmind G, Blundell C, Lillicrap T, Kavukcuoglu K, Wierstra D (2016) Matching networks for one shot learning. In: NIPS – reference: Plesse F, Ginsca A, Delezoide B, Prêteux F (2020) Focusing visual relation detection on relevant relations with prior potentials. In: WACV – reference: Simonyan K, Zisserman A (2015) Very deep convolutional networks for Large-Scale image recognition. 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| SubjectTerms | Combinatorial analysis Computer Communication Networks Computer Science Computer Vision and Pattern Recognition Data Structures and Information Theory Modelling Multimedia Multimedia Information Systems Neural networks Object recognition Prototypes Special Purpose and Application-Based Systems Training |
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| Title | Modelling relations with prototypes for visual relation detection |
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