The DOI matches its Crossref record; 221 of the 223 references it cites were found in a registry or an index.
Multiple instance learning: A survey of problem characteristics and applications
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“With over 30 billion sequences in total, this represents one of the largest multiple instance learning experiments ever conducted (Carbonneau et al., 2018).”
From Hopfield Networks is All You Need (2020) · cites this paper as Carbonneau et al., 2018
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Items 201–221 of 221
Combining example selection with instance selection to speed up multiple-instance learning
Yuan, L., Liu, J., & Tang, X. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.09.008 · Cited by 11 · cited 1×
“ple, [186] uses instance selection algorithms inspired by the immune system to reduce the size of the data set before using MIL algorithms.”
MethodMultiple Instance Learning with Genetic Programming for Web Mining
Zafra, A., Ventura, S., Herrera‐Viedma, E., & Romero, C. (2007) · Springer eBooks · doi:10.1007/978-3-540-73007-1_111 · Cited by 10 · cited 1×
“MIL has been used for web index-page recommendations based on a user browsing history [131, 132].”
MethodMultiple-instance discriminant analysis
Chai, J., Ding, X., Chen, H., & Li, T. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.02.002 · Cited by 9
Witness identification in multiple instance learning using random subspaces
Carbonneau, M., Granger, É., & Gagnon, G. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900199 · Cited by 9 · cited 3×
“RSIS [30]: This method probabilistically identifies the witnesses in positive bags using a procedure based on random subspacing and clustering introduced in [53].”
BackgroundMethodSelf-citationFast multiple instance learning via L1,2 logistic regression
Fu, Z., & Robles‐Kelly, A. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761294 · Cited by 8
A Sphere-Description-Based Approach for Multiple-Instance Learning
Xiao, Y., Liu, B., & Hao, Z. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2539952 · Cited by 8 · cited 4×
“The forementioned methods were proposed for instance classification ut are not different in spirit from most bag classification methds reasoning in the instance space like APR [3] , EM-DD [35] , MIptimalBall [63] , MI-SVM [6] and SDB-MIL [31] .”
BackgroundMethodInfluentialFAMER: Making Multi-Instance Learning Better and Faster
Ping, W., Xu, Y., Wang, J., & Hua, X. (2011) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972818.51 · Cited by 8 · cited 1×
“Many of these methods, like BoW [60, 70], miFV [62], FAMER [71] or PPMM [72] represent bags as instance distributions which indirectly account for co-occurrence.”
MethodSALE: Self-adaptive LSH encoding for multi-instance learning
Xu, D., Wu, J. X., Li, D., Tian, Y., Xingquan Zhu, A., & Wu, X. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.04.029 · Cited by 8 · cited 1×
“In [95,185] , hash unctions have been used to accelerate the bag encoding process.”
MethodExploring Features for Complicated Objects
Wu, J. X., Hong, Z., Pan, S., Xingquan Zhu, A., Cai, Z., & Zhang, C. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2662041 · Cited by 7
Sparse multiple instance learning as document classification
Yan, S., Zhu, X., Liu, G., & Wu, J. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3567-z · Cited by 7
Multiple-Instance Learning with Structured Bag Models
Warrell, J., & Torr, P. H. S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23094-3_27 · Cited by 6 · cited 1×
“Spatial dependency in images have also been modeled in with CRF in [74, 82].”
MethodMulti-instance Methods for Partially Supervised Image Segmentation
Müller, A., & Behnke, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28258-4_12 · Cited by 6 · cited 1×
“Efforts were dedicated to localize objects and segment them at pixel-level using traditional segmentation algorithms such as Constraint Parametric Min-Cuts [110], JSEG [74] or Multi-scale combinatorial grouping [111].”
MethodVocabulary-Based Approaches for Multiple-Instance Data: A Comparative Study
Amores, J. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.1032 · Cited by 6 · cited 20×
“Methods which analyze instance distributions in bags [70, 71, 72] may also have problems dealing with low WR because distributions in positive and negative bags become similar.”
BackgroundMethodInfluentialLocal dimensionality reduction for multiple instance learning
Kim, S., & Choi, S. (2010) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2010.5589175 · Cited by 6 · cited 1×
“ods [88, 89, 90, 91] where bag classification accuracy is maximized.”
BackgroundLearning Instance Concepts from Multiple-Instance Data with Bags as Distributions
Doran, G. B., & Ray, S. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.9016 · Cited by 5 · cited 2×
“Methods representing bags as distributions [70, 71, 116] can naturally deal with noisy instances because a single positive instance does not significantly 830”
BackgroundUsing the Multiple Instance Learning framework to address differential regulation
Palachanis, D. K. (2014) · Research Repository (Delft University of Technology) · Cited by 4
Learning with only multiple instance positive bags
Wang, Z., Zhao, Z., & Zhang, C. (2016) · doi:10.1109/ijcnn.2016.7727217 · Cited by 3 · cited 1×
“Consequently, several methods using one-class SVM have been proposed [112, 113, 114].”
MethodFast Multiple Instance Learning Via L1,2 Logistic Regression
Fu, Z., & Robles‐Kelly, A. (2012) · Cited by 3
Multiple Instance Multiple Label Learning
Herrera, F., Ventura, S., Bello, R., Chris Cornelis, A., Zafra, A., Sánchez-Tarragó, D., & Vluymans, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-47759-6_10 · Cited by 2
Exploring features for complicated objects: Cross-view feature selection for multi-instance learning
Wu, J., Hong, Z., Pan, S., Xingquan Zhu, A., Cai, Z., & Zhang, C. (2014) · UTS ePRESS (University of Technology Sydney) · Cited by 2
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled BagsPreprint
Bao, H., Sakai, T., Sato, I., & Sugiyama, M. (2017) · arXiv (Cornell University) · Cited by 1
Research works printed but not linked: 1
- Babenko, 2011, Robust object tracking with online multiple instance learning, IEEE Trans. Pattern Anal. Mach. Intell., 33, 1619, 10.1109/TPAMI.2010.226 · DOI · Paper page
Cited by
Sorted by citations
Attention-based Deep Multiple Instance LearningPreprint
Ilse, M., Tomczak, J. M., & Welling, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04712 · Cited by 1,428
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
“We refer the readers to [4] for a survey.”
BackgroundArtificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777
Zhong, J.-X., Li, N., Kong, W., Liu, S., Li, T. H., & Li, G. (2019) · 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.2019.00133 · Cited by 626
“…that the label Y = 0 is noiseless, since it means all snippets v i in the video V are normal: However, Y = 1 is noisy because in this case the video V is partially made up of anomalous clips: This is referred to as one-sided label noise [7, 9, 55], for the noise only appears along with Y = 1 .”
BackgroundFederated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518
Metadata
- Authors
- Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G.
- Affiliations
- École de Technologie Supérieure (CA)
- Erasmus MC (NL)
- Corresponding author
- Carbonneau, Marc-André
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 77
- Pages
- 329-353
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2560886373
- Cited by
- 740
- References
- 223
- Citations by year
- 2026: 75 · 2025: 118 · 2024: 91 · 2023: 124 · 2022: 98 · 2021: 92 · 2020: 84 · 2019: 41 · 2018: 14
- FWCI
- 22.10
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Pattern Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- TU/e Research Portal (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Espace ÉTS (ETS) (submittedVersion)
- TU/e Research Portal (submittedVersion, other-oa)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- multiple instance learning, weakly supervised learning, problem characteristics, data distribution, label ambiguity, computer vision, document classification
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