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Mixed Supervised Object Detection with Robust Objectness Transfer
Li Y(李岩)1,2; Zhang JG(张俊格)1,2; Huang KQ(黄凯奇)1,2,4; Zhang JG(张建国)3
Source PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
2019-03
Volume41Issue:3Pages:639-653
Abstract

In this paper, we consider the problem of leveraging existing fully labeled categories to improve the weakly supervised detection (WSD) of new object categories, which we refer to as mixed supervised detection (MSD). Different from previous MSD methods that directly transfer the pre-trained object detectors from existing categories to new categories, we propose a more reasonable and robust objectness transfer approach for MSD. In our framework, we first learn domain-invariant objectness knowledge from the existing fully labeled categories. The knowledge is modeled based on invariant features that are robust to the distribution
discrepancy between the existing categories and new categories; therefore the resulting knowledge would generalize well to new categories and could assist detection models to reject distractors (e.g., object parts) in weakly labeled images of new categories. Under the guidance of learned objectness knowledge, we utilize multiple instance learning (MIL) to model the concepts of both objects and distractors and to further improve the ability of rejecting distractors in weakly labeled images. Our robust objectness transfer approach outperforms the existing MSD methods, and achieves state-of-the-art results on the challenging ILSVRC2013 detection
dataset and the PASCAL VOC datasets.

KeywordWeakly Supervised Detection Mixed Supervised Detection Robust Objectness Transfer
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23345
Collection智能感知与计算研究中心
Corresponding AuthorHuang KQ(黄凯奇)
Affiliation1.中国科学院自动化研究所
2.University of Chinese Academy of Sciences
3.Computing, School of Science and Engineering, Univerisity of Dundee, UK
4.CAS Center for Excellence in Brain Science and Intelligence Technology
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li Y,Zhang JG,Huang KQ,et al. Mixed Supervised Object Detection with Robust Objectness Transfer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,41(3):639-653.
APA Li Y,Zhang JG,Huang KQ,&Zhang JG.(2019).Mixed Supervised Object Detection with Robust Objectness Transfer.IEEE Transactions on Pattern Analysis and Machine Intelligence,41(3),639-653.
MLA Li Y,et al."Mixed Supervised Object Detection with Robust Objectness Transfer".IEEE Transactions on Pattern Analysis and Machine Intelligence 41.3(2019):639-653.
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