The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Weakly- and Semi-Supervised Fast Region-Based CNN for Object Detection
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Faster R-CNN detection accuracy is higher than SSD, Fast R-CNN, and other algorithms, verified on multiple datasets.
“The detection accuracy of Faster R-CNN is higher than SSD [32, 33], Fast R-CNN [34], and other algorithms verified on multiple datasets.”
From A New Steel Defect Detection Algorithm Based on Deep Learning (2021) · cites this paper as [34]
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
(2016) · Cited by 31,590
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
Self-citationYOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Selective Search for Object Recognition
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980
R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint
Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034
Solving the multiple instance problem with axis-parallel rectangles
Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00034-3 · Cited by 2,866
HCP: A Flexible CNN Framework for Multi-Label Image Classification
Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., Zhao, Y., & Yan, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2491929 · Cited by 843
Richer Convolutional Features for Edge Detection
Liu, Y., Cheng, M., Hu, X., Bian, J.-W., Zhang, L., Bai, X., & Tang, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2878849 · Cited by 786
Multiple Instance Boosting for Object Detection
Zhang, C., Platt, J. C., & Viola, P. A. (2005) · neural information processing systems · Cited by 687
Holistically-nested edge detection
Xie, S., & Tu, Z. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1004-z · Cited by 682
DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299024 · Cited by 554
Self-citationDiagnosing Error in Object Detectors
Hoiem, D., Chodpathumwan, Y., & Dai, Q. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_25 · Cited by 520
Multiple Instance Detection Network with Online Instance Classifier Refinement
Tang, P., Wang, X., Bai, X., & Liu, W. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.326 · Cited by 503
Self-citationWeakly Supervised Object Localization with Multi-Fold Multiple Instance Learning
Cinbis, R. G., Verbeek, J., & Schmid, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2535231 · Cited by 420
Deep multiple instance learning for image classification and auto-annotation
Wu, J., Yu, Y., Huang, C., & Yu, K. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298968 · Cited by 414
Weakly Supervised Cascaded Convolutional Networks
Diba, A., Sharma, V., Pazandeh, A., Pirsiavash, H., & Van Gool, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.545 · Cited by 287
Deep Self-Taught Learning for Weakly Supervised Object Localization
Jie, Z., Wei, Y., Jin, X., Feng, J., & Liu, W. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.457 · Cited by 209
Self-citationUnsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning
Zhu, J.-Y., Wu, J., Xu, Y. L., Chang, E., & Zhuowen Tu, A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2353617 · Cited by 135
Training Object Class Detectors with Click Supervision
Papadopoulos, D. P., Uijlings, J., Keller, F., & Ferrari, V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.27 · Cited by 116
Relaxed Multiple-Instance SVM with Application to Object Discovery
Wang, X., Zhu, Z., Yao, C., & Bai, X. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.145 · Cited by 88
Self-citationDeep feature based contextual model for object detection
Chu, W., & Cai, D. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.048 · Cited by 68
Patch Alignment Manifold Matting
Li, X., Liu, K., Dong, Y., et al. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2727140 · Cited by 46
Convolutional Edge Constraint-Based U-Net for Salient Object Detection
Le, H., Li, X., & Dong, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2910572 · Cited by 36
Superpixel-Based Foreground Extraction With Fast Adaptive Trimaps
Li, X., Liu, K., & Dong, Y. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2747143 · Cited by 34
Saliency Guided End-to-End Learning for Weakly Supervised Object DetectionPreprint
Lai, B., & Gong, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06768 · Cited by 15
Flagged first, then by citations
Research works printed but not linked: 5
- Lin T Y, Dollr P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. arXiv:1612.03144, 2016. https://arxiv.org/pdf/16-12.03144.pdf, August 2019. · arXiv
- Bilen H, Vedaldi A. Weakly supervised deep detection net- works. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 2016, pp.2846-2854. · DOI · Paper page
- Papadopoulos D P, Uijlings J R, Keller F, Ferrari V. We don’t need no bounding-boxes: Training object class detectors using only human verification. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 2016, pp.854-863. · DOI · Paper page
- Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In Proc. the 3rd International Conference on Learning Representations, May 2015, Article No. 4.
- Everingham M, Zisserman A, Williams C K, Van Gool L, Allan M, Bishop C M, Chapelle O, Dalal N, Deselaers T, Dorkó G. The pascal visual object classes challenge 2007 (voc2007) results. http://www.pascal-network.org/challen-ges/VOC/voc2007/workshop/index.html, Sept. 2019.
Cited by
Sorted by citations
A New Steel Defect Detection Algorithm Based on Deep Learning
Zhao, W., Chen, F., Huang, H., Li, D., & Cheng, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5592878 · Cited by 259
“Faster R-CNN is the mainstream two-stage target detection algorithm, which is used in face detection and defect detection widely. detection accuracy of Faster R-CNN is higher than SSD [32, 33], Fast R-CNN [34], and other algorithms verified on multiple datasets.”
Method
Metadata
- Authors
- Wang, X.-G., Wang, J.-S., Tang, P., & Liu, W.-Y.
- Affiliations
- Huazhong University of Science and Technology (CN)
- ORCID
- Tang, Peng
- Venue
- Journal of Computer Science and Technology
- Published
- 2019-11
- Volume
- 34
- Issue
- 6
- Pages
- 1269-1278
- ISSN
- 1000-9000 (ISSN-L)
- 1860-4749
- OpenAlex
- W2990448940
- Cited by
- 8
- References
- 32
- Citations by year
- 2025: 2 · 2023: 3 · 2021: 2 · 2020: 1
- FWCI
- 0.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Science and Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- object detection, weakly supervised learning, semi-supervised learning, Fast R-CNN, R-CNN, image-level annotations, bounding box annotation, annotation cost reduction, Pascal VOC 2007
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
