The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Multiple Instance Detection Network with Online Instance Classifier Refinement
Open accessAbstract
Of late, weakly supervised object detection is with great importance in object recognition. Based on deep learning, weakly supervised detectors have achieved many promising results. However, compared with fully supervised detection, it is more challenging to train deep network based detectors in a weakly supervised manner. Here we formulate weakly supervised detection as a Multiple Instance Learning (MIL) problem, where instance classifiers (object detectors) are put into the network as hidden nodes. We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level supervision, i.e., without object location information. More precisely, instance labels inferred from weak supervision are propagated to their spatially overlapped instances to refine instance classifier online. The iterative instance classifier refinement procedure is implemented using multiple streams in deep network, where each stream supervises its latter stream. Weakly supervised object detection experiments are carried out on the challenging PASCAL VOC 2007 and 2012 benchmarks. We obtain 47% mAP on VOC 2007 that significantly outperforms the previous state-of-the-art.
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Cites
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Cited by
Li, B., Li, Y., & Eliceiri, K. W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01409 · Cited by 957
Ahn, J., & Kwak, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00523 · Cited by 872
Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation
Inoue, N., Furuta, R., Yamasaki, T., & Aizawa, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00525 · Cited by 616
Detecting Twenty-Thousand Classes Using Image-Level Supervision
Zhou, X., Girdhar, R., Joulin, A., Krähenbühl, P., & Misra, I. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20077-9_21 · Cited by 564
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
Tang, P., Wang, X., Bai, S., Shen, W., Bai, X., Liu, W., & Yuille, A. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2876304 · Cited by 406
Self-citation
Metadata
- Authors
- Tang, P., Wang, X., Bai, X., & Liu, W.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3059-3067
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2604260814
- Cited by
- 496
- References
- 10
- FWCI
- 11.81
- Type
- conference paper
- Language
- en
- Open access
- green
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- Tang, P., Wang, X., Bai, X., & Liu, W. (2017). Multiple Instance Detection Network with Online Instance Classifier Refinement. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.326
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