Abstract
Fully supervised object detection has achieved great success in recent years. However, abundant bounding boxes annotations are needed for training a detector for novel classes. To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes which only requires the web images without further annotations. Our proposed method combines bottom-up and top-down cues for novel class detection. Within our approach, we introduce a bottom-up mechanism based on the well-trained fully supervised object detector (i.e. Faster RCNN) as an object region estimator for web images by recognizing the common objectiveness shared by base and novel classes. With the estimated regions on the web images, we then utilize the top-down attention cues as the guidance for region classification. Furthermore, we propose a residual feature refinement (RFR) block to tackle the domain mismatch between web domain and the target domain. We demonstrate our proposed method on PASCAL VOC dataset with three different novel/base splits. Without any target-domain novel-class images and annotations, our proposed webly supervised object detection model is able to achieve promising performance for novel classes. Moreover, we also conduct transfer learning experiments on large scale ILSVRC 2013 detection dataset and achieve state-of-the-art performance.
Original language | English |
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Title of host publication | Proceedings - 33th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2020 |
Editors | Ce Liu, Greg Mori, Kate Saenko, Silvio Savarese |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 12933-12942 |
Number of pages | 10 |
ISBN (Electronic) | 9781728171685 |
ISBN (Print) | 9781728171692 |
DOIs | |
Publication status | Published - 2020 |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2020 - Virtual, China Duration: 14 Jun 2020 → 19 Jun 2020 http://cvpr2020.thecvf.com (Website ) https://openaccess.thecvf.com/CVPR2020 (Proceedings) https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding (Proceedings) |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISSN (Print) | 1063-6919 |
ISSN (Electronic) | 2575-7075 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2020 |
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Abbreviated title | CVPR 2020 |
Country/Territory | China |
City | Virtual |
Period | 14/06/20 → 19/06/20 |
Internet address |
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