Abstract
This paper presents a weakly supervised object detection method based on activity label and relationship modeling, which is motivated by the assumption that configuration of human and object are similar in same activity, and joint modeling of human, active object and activity could leverage the recognition of them. Compared to most weakly supervised method taking object as independent instance, firstly, active human and object proposals are learned and filtered based on class activation map of multi-label classification. Secondly, a spatial relationship prior including relative position, scale, overlaps etc are learned dependent on action. Finally, a multi-stream object detection framework integrating the spatial prior and pairwise ROI pooling are proposed to jointly learn the object and action class. Experiments are conducted on HICO-DET dataset, and our approach outperforms the state of the art weakly supervised object detection methods. © 2020 IEEE
| Original language | English |
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| Title of host publication | Proceedings of ICPR 2020 |
| Subtitle of host publication | 25th International Conference on Pattern Recognition |
| Publisher | IEEE |
| Pages | 9628-9634 |
| ISBN (Electronic) | 9781728188089 |
| ISBN (Print) | 978-1-7281-8809-6 |
| DOIs | |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 25th International Conference on Pattern Recognition (ICPR2020) - Virtual, Milan, Italy Duration: 10 Jan 2021 → 15 Jan 2021 https://www.micc.unifi.it/icpr2020/ |
Publication series
| Name | Proceedings - International Conference on Pattern Recognition |
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| ISSN (Print) | 1051-4651 |
Conference
| Conference | 25th International Conference on Pattern Recognition (ICPR2020) |
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| Abbreviated title | ICPR 2020 |
| Place | Italy |
| City | Milan |
| Period | 10/01/21 → 15/01/21 |
| Internet address |
Funding
This work is partly supported by the National Key Research and Development Program of China (2017YFB1300200, 2017YFB1300203), the National Natural Science Foundation of China (Grant no. 61702516, 51705515, 61627808), the Joint Research Fund between the National Natural Science Foundation of China (NSFC) and Shen Zhen (Grant no. U1713201) and the Research Fund from Science and Technology on Underwater Vehicle Technology Laboratory (No. 6142215190103).