Cited by 740Machine Learning and AlgorithmsMachine Learning and Data ClassificationDomain Adaptation and Few-Shot Learning

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Multiple instance learning: A survey of problem characteristics and applications

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Carbonneau, Marc-André, Cheplygina, Veronika, Granger, Eric, Gagnon, Ghyslain·2018·Pattern Recognition

DOI 10.1016/j.patcog.2017.10.009

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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · cited 2×
“There are external methods like SMOTE [193] and RUSBoost [194] that necessitate accurate labels to perform over or under sampling.”
Method
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · cited 1×
“The method is essentially the same as gradient boosting [143] except that the loss function is based on bag classification error.”
Method
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · cited 1×
“Recently, methods for representation learning [160] have gained in popularity because they usually yield a high level of accuracy.”
Method
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,051
Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881 · cited 1×
“The experiments were conducted using a nested cross-fold validation protocol [149].”
Method
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916 · cited 1×
“In BoW models for computer vision, this can be achieved by dividing the images [102, 103] or videos [80] into different spatial and/or temporal zones.”
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 5,547
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Distributional Structure
Harris, Z. S. (1954) · WORD · doi:10.1080/00437956.1954.11659520 · Cited by 4,807
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · 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.2008.4587756 · Cited by 3,481 · cited 2×
“In BoW models, this can be achieved by dividing the images [78, 79] or videos [75] into different spatial and/or temporal zones.”
Background
Estimating attributes: Analysis and extensions of RELIEF
Kononenko, I. (1994) · Lecture notes in computer science · doi:10.1007/3-540-57868-4_57 · Cited by 3,062 · cited 1×
“of the Relief [82] feature selection algorithm for MIL.”
Method
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
International Conference on Machine Learning (ICML)-2005
津田, 宏. (2006) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 2,558
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
Alcalá‐Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., & Herrera, F. (2011) · Cited by 2,337
Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08
(2008) · doi:10.3115/1613715 · Cited by 2,216
NewsWeeder: Learning to Filter Netnews
Lang, K. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50048-7 · Cited by 2,119 · cited 3×
“Even if the Birds and the Newsgroups data sets both possess low WR, it would seem that supervised methods are better suited for this task than MIL methods which use bag accuracy as an optimization objective (MILES, EMDD and MIL Boost).”
Method
Robust object tracking with online multiple instance learning
Babenko, B., Yang, M.-H., & Belongie, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.226 · Cited by 2,007
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
Seiffert, C., Khoshgoftaar, T. M., Hulse, J. V., & Napolitano, A. (2009) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2009.2029559 · Cited by 1,914 · cited 1×
“There are external methods like SMOTE [156] and RUSBoost [157] that necessitate accurate labels to perform over or under sampling.”
Method
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777 · cited 1×
“All methods in this experiment use an SVM which is known to be vulnerable to label noise [172].”
Method
Frustratingly Easy Domain AdaptationPreprint
Daumé, H. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0907.1815 · Cited by 1,535 · cited 1×
“Instance could be mapped to the same semantic space to be compared directly, graph model could be used to aggregate several heterogeneous descriptors or instances could be combined in pairs to create new spaces for comparison similarly to [169].”
Background
The pyramid match kernel: discriminative classification with sets of image features
Grauman, K. L., & Darrell, T. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.239 · Cited by 1,500 · cited 1×
“In BoW models, this can be achieved by dividing the images [78, 79] or videos [75] into different spatial and/or temporal zones.”
Background
Support Vector Machines for Multiple-Instance Learning
Andrews, S. J., Tsochantaridis, I., & Hofmann, T. (2002) · Cited by 1,404
From captions to visual concepts and back
Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (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.2015.7298754 · Cited by 1,321
Evaluation of local spatio-temporal features for action recognition
Wang, H., Ullah, M. M., Kläser, A., Laptev, I., & Schmid, C. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.124 · Cited by 1,295 · cited 1×
“This agrees with other bag-of-words (BoW) empirical comparisons [50, 51].”
Background
A Framework for Multiple-Instance Learning
Maron, O., & Lozano‐Pérez, T. (1997) · Cited by 1,232
Simultaneous Detection and Segmentation
Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_20 · Cited by 1,189 · 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].”
Method
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2017)
Amendola, G., Ricca, F., & Truszczyński, M. (2017) · International Joint Conference on Artificial Intelligence · Cited by 1,179
A Survey of Predictive Modeling on Imbalanced Domains
Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129 · cited 1×
“There exist many methods to deal with imbalanced data [155].”
Method
Exploring Strategies for Training Deep Neural Networks
Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. (2009) · doi:10.1145/1577069.1577070 · Cited by 1,111
Sampling Strategies for Bag-of-Features Image Classification
Nowak, E., Jurie, F., & Triggs, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_38 · Cited by 927 · cited 1×
“This agrees with other bag-of-words (BoW) empirical comparisons [50, 51].”
Background
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
(2018) · doi:10.1184/r1/6475985.v1 · Cited by 851
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
Li, L.-J., Su, H., Fei-Fei, L., & Xing, E. P. (2018) · KiltHub Repository · doi:10.1184/r1/6475985 · Cited by 831
Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions
Ringeval, F., Sonderegger, A., Sauer, J., & Lalanne, D. (2013) · 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2013.6553805 · Cited by 771
MILES: Multiple-Instance Learning via Embedded Instance Selection
Chen, Y., Bi, J., & Wang, J. Z. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.248 · Cited by 750 · cited 4×
“MILES [4]: In Multiple-Instance Learning via Embedded instance Selection (MILES) an SVM classifies bags represented by a feature vectors containing maximal similarities to selected prototypes.”
MethodInfluential
Discriminative learned dictionaries for local image analysis
Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2008) · 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.2008.4587652 · Cited by 735 · cited 1×
“Some of these methods learn features in a supervised manner to obtain a more discriminative representation [161], or, in deep learning, a supervised training phase is often used to fine tune the features learned in an unsupervised manner [162].”
Method
Controlling the Sensitivity of Support Vector Machines
Veropoulos, K., Campbell, I. C. G., & Cristianini, N. (1999) · Cited by 708
Action bank: A high-level representation of activity in video
Sadanand, S., & Corso, J. J. (2012) · 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.2012.6247806 · Cited by 696 · cited 1×
“between low-level features and concepts [203, 204, 205].”
Multiple Instance Boosting for Object Detection
Zhang, C., Platt, J. C., & Viola, P. A. (2005) · neural information processing systems · Cited by 687
Image Categorization by Learning and Reasoning with Regions
Chen, Y., & Wang, J. Z. (2004) · doi:10.5555/1005332.1016789 · Cited by 628
EM-DD: An Improved Multiple-Instance Learning Technique
Zhang, Q., & Goldman, S. A. (2001) · Cited by 626
Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities
Ryoo, M. S., & Aggarwal, J. K. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459361 · Cited by 582 · cited 1×
“Capturing this structure may lead to better classification performance [10, 75, 76].”
Background
Solving Multiple-Instance Problem: A Lazy Learning Approach
Wang, J., Zucker, E. J.-D., & Zucker, J. (2000) · CogPrints (University of Southampton) · Cited by 570
Multiple-Instance Learning for Natural Scene Classification
Maron, O., & Ratan, A. L. (1998) · Cited by 565
Multiple instance classification: Review, taxonomy and comparative study
Amores, J. (2013) · Artificial Intelligence · doi:10.1016/j.artint.2013.06.003 · Cited by 562
Multi-Instance Kernels
Gärtner, T., Flach, P. A., Kowalczyk, A., & Smola, A. J. (2002) · Cited by 460
Multiple-Instance Active Learning
Settles, B., Craven, M. W., & Ray, S. (2007) · Cited by 458
Multi-instance multi-label learning
Zhou, Z., Zhang, M.-L., Huang, S.-J., & Li, Y.-F. (2011) · Artificial Intelligence · doi:10.1016/j.artint.2011.10.002 · Cited by 424 · cited 1×
“This idea has been the object of several publications [37, 38, 39].”
Background
Weakly 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 · cited 1×
“Also, several authors noted that in this context, MIL algorithms are sensitive to initialization [9,107].”
Background
Letter Recognition Using Holland-Style Adaptive Classifiers
Frey, P. W., & Slate, D. J. (1991) · Machine Learning · doi:10.1023/a:1022606404104 · Cited by 417
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 · cited 1×
“The weak supervision can come from description sentences [103–105], web search engine results [106], tags associated with similar images and words found on web pages associated with the images [2].”
Background
Multi-instance learning by treating instances as non-I.I.D. samples
Zhou, Z., Sun, Y., & Li, Y.-F. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553534 · Cited by 399 · cited 8×
“To deal with similar instances, miGraph [10] builds a graph per bag and groups similar instances together to adjust their relative importance based on the group size.”
BackgroundMethodInfluential
A review of multi-instance learning assumptions
Foulds, J. R., & Frank, E. (2010) · The Knowledge Engineering Review · doi:10.1017/s026988890999035x · Cited by 397
Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005
(2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1555790 · Cited by 382
Proceedings of International Conference on Acoustics Speech and Signal Processing ICASSP’98
McKay, I., Douther, C., & Jack, M. A. (1998) · Cited by 379
Support Vector Machine Solvers
Bottou, L., & Lin, C. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7496.003.0003 · Cited by 346 · cited 1×
“For example, the computational complexity of 1915 an SVM is between O(n2) and O(n3) when using traditional QP and LP solvers [180], where n is the number of instances.”
Method
From Group to Individual Labels Using Deep Features
Kotzias, D., Denil, M., de Freitas, N., & Smyth, P. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783380 · Cited by 324 · cited 1×
“In [40, 125, 129, 130], MIL was used to infer the sentiment expressed in individual sentences based on the labels provided for entire user reviews.”
Method
Weakly supervised histopathology cancer image segmentation and classification
Xu, Y. L., Zhu, J.-Y., Chang, E., Lai, M., & Tu, Z. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.01.010 · Cited by 286 · cited 1×
“The iseases and image modalities used are very diverse; applications nclude classification of cancer in histopathology images [141] , diaetes in retinal images [142] , dementia in brain MR [143] , tubercuosis in X-ray images [144] , classification of a chronic lung disease n CT [145] and others.”
Background
Parameterized neural networks for high-energy physics
Baldi, P., Cranmer, K. S., Faucett, T., Sadowski, P., & Whiteson, D. (2016) · The European Physical Journal C · doi:10.1140/epjc/s10052-016-4099-4 · Cited by 285
Learning to Extract Relations from the Web using Minimal Supervision
Bunescu, R., & Mooney, R. J. (2007) · Cited by 265
Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application
Murray, J. F., Hughes, G. F., & Kreutz-Delgado, K. (2005) · doi:10.5555/1046920.1088699 · Cited by 260
Multiple-Instance Learning for Medical Image and Video Analysis
Quellec, G., Cazuguel, G., Cochener, B., & Lamard, M. (2017) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2017.2651164 · Cited by 259
Real-time visual tracking via online weighted multiple instance learning
Zhang, K., & Song, H. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.07.013 · Cited by 257 · cited 1×
“Also in videos, MIL methods were proposed to perform object tracking [61, 138, 139].”
Method
Content-Based Image Retrieval Using Multiple-Instance Learning
Zhang, Q., Goldman, S. A., Yu, W., & Fritts, J. E. (2002) · Cited by 243
Correlated Label Propagation with Application to Multi-label Learning
Kang, F., Jin, R., & Sukthankar, R. (2006) · 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.2006.90 · Cited by 231 · cited 1×
“Thus, the observation of nature segments might help to decide if the image contains a cocktail or a bear [68].”
Background
Rank-loss support instance machines for MIML instance annotation
Briggs, F., Fern, X. Z., & Raich, R. (2012) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339616 · Cited by 223 · cited 4×
“From these few data sets, to our knowledge, there is only one (Birds [12]) that supply instance labels and is non-artificial.”
BackgroundMethodInfluential
Supervised versus multiple instance learning
Ray, S., & Craven, M. W. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102439 · Cited by 222 · cited 6×
“Ray and Craven [18] compared the performance of MIL methods against supervised methods on MIL problems.”
BackgroundMethodInfluential
Joint multi-label multi-instance learning for image classification
Zha, Z.-J., Hua, X., Mei, T., Wang, J., Qi, G.-J., & Wang, Z. (2008) · 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.2008.4587384 · Cited by 222 · cited 3×
“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].”
Method
Logistic Regression and Boosting for Labeled Bags of Instances
Xu, X., & Frank, E. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24775-3_35 · Cited by 220 · cited 1×
“Also, some methods represent bags by the average of the instances they contain, like NSK-SVM [63], or by considering their contribution to the bag label equally [64].”
Method
Simultaneous Detection and SegmentationPreprint
Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.1808 · Cited by 216 · 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].”
Method
Multiple instance learning for classification of dementia in brain MRI
Tong, T., Wolz, R., Gao, Q., Guerrero, R., Hajnal, J. V., & Rueckert, D. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.04.006 · Cited by 213
On learning to localize objects with minimal supervisionPreprint
Song, H. O., Girshick, R., Jegelka, S., Mairal, J., Harchaoui, Z., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.1024 · Cited by 196 · cited 1×
“Also, several authors noted that in this context, MIL algorithms are sensitive to initialization [9,107].”
Background
Perturbation Biology: Inferring Signaling Networks in Cellular Systems
Molinelli, E. J., Korkut, A., Wang, W., Miller, M. L., Gauthier, N. P., Jing, X., Kaushik, P., He, Q., Mills, G. B., Solit, D. B., et al. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003290 · Cited by 194
Multiple instance learning for sparse positive bags
Bunescu, R., & Mooney, R. J. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273510 · Cited by 193 · cited 3×
“For example, the scenario where the number of positive instances in a bag is low was referred to as either sparse bags [26, 27] or low witness rate [24, 28].”
BackgroundMethod
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
Hauptmann, A. G., Yan, R., Lin, W., Christel, M. G., & Wactlar, H. D. (2007) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2007.900150 · Cited by 190 · cited 1×
“between low-level features and concepts [203, 204, 205].”
MILIS: Multiple Instance Learning with Instance Selection
Fu, Z., Robles‐Kelly, A., & Zhou, J. W. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.155 · Cited by 182
Bag Constrained Structure Pattern Mining for Multi-Graph Classification
Wu, J. X., Xingquan Zhu, A., Chengqi Zhang, B., & Yu, P. S. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.2297923 · Cited by 170
On the relation between multi-instance learning and semi-supervised learning
Zhou, Z., & Xu, J.-M. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273643 · Cited by 163 · cited 1×
“Some methods label all instances independently, like mi-SVM [6] and MissSVM [58].”
Method
A Two-Level Learning Method for Generalized Multi-instance Problems
Weidmann, N., Frank, E., & Pfahringer, B. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39857-8_42 · Cited by 162 · cited 1×
“levels of generality for multiple concept assumptions of this type [32].”
Background
Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning
Yang, C., Dong, M., & Hua, J. (2006) · 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.2006.250 · Cited by 162 · cited 2×
“In [57], a different weight is attributed to FP and FN during the optimization of an SVM.”
Background
Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning
Vezhnevets, A., & Buhmann, J. M. (2010) · 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.2010.5540060 · Cited by 161
Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors
Stikic, M., Larlus, D., Ebert, S., & Schiele, B. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.36 · Cited by 155 · cited 1×
“For instance, in [105, 160] MIL is used to recognize human activities from wearable body sensors.”
Background
JCLEC: a Java framework for evolutionary computation
Ventura, S., Romero, C., Zafra, A., Delgado, J. A. S., & Hervás‐Martínez, C. (2007) · Soft Computing · doi:10.1007/s00500-007-0172-0 · Cited by 153 · cited 1×
“There are also machine learning and data mining software packages such as Weka [176], KEEL [177] and JCLEC [178] for which MIL modules exist.”
Method
Solving multi-instance problems with classifier ensemble based on constructive clustering
Zhou, Z., & Zhang, M.-L. (2006) · Knowledge and Information Systems · doi:10.1007/s10115-006-0029-3 · Cited by 150
Multi-instance clustering with applications to multi-instance prediction
Zhang, M.-L., & Zhou, Z. (2008) · Applied Intelligence · doi:10.1007/s10489-007-0111-x · Cited by 144 · cited 2×
“k -Medoids and the Hausdorff distance [48]).”
Method
z-SVM: An SVM for Improved Classification of Imbalanced Data
Imam, T., Ting, K. M., & Kamruzzaman, J. (2006) · Lecture notes in computer science · doi:10.1007/11941439_30 · Cited by 142
MIForests: Multiple-Instance Learning with Randomized Trees
Leistner, C., Saffari, A., & Bischof, H. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_3 · Cited by 141
Multi-Instance Learning Based Web Mining
Zhou, Z., Jiang, K., & Li, M. (2005) · Applied Intelligence · doi:10.1007/s10489-005-5602-z · Cited by 140
Multiple instance learning with bag dissimilarities
Cheplygina, V., Tax, D. M. J., & Loog, M. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.07.022 · Cited by 139 · cited 3×
“In [69], it is shown that different birds are often heard in the same audio fragment, so a “negative” bird song could help to correctly classify the bird of interest.”
BackgroundMethodSelf-citation
Multiple Instance Regression
Ray, S., & Page, D. (2001) · Cited by 138
Semantic Model Vectors for Complex Video Event Recognition
Merler, M., Huang, B., Xie, L., Hua, G., & Natsev, A. (2011) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2168948 · Cited by 137 · cited 1×
“Alternatively, events in videos can be represented, among others, by frame, texture and motion descriptors [167, 168].”
Background
A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays
Melendez, J., van Ginneken, B., Maduskar, P., Philipsen, R. H. H. M., Reither, K., Breuninger, M., Adetifa, I., Maane, R., Ayles, H., & Sá‎nchez, C. I. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2350539 · Cited by 136 · cited 5×
“Quantitative evaluation is performed in [58, 142, 144].”
BackgroundInfluential
Unsupervised 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
Bayesian multiple instance learning
Raykar, V. C., Krishnapuram, B., Bi, J., Dündar, M., & Rao, R. B. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390258 · Cited by 134
Video Event Detection by Inferring Temporal Instance Labels
Lai, K.-T., Yu, F. X., Chen⋆, M.-S., & Chang, S. (2014) · 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.2014.288 · Cited by 126 · cited 1×
“It has been used to recognize complex events such as “attempting a board trick” or “birthday party” [8, 113].”
Method
MISSL
Rahmani, R., & Goldman, S. A. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143933 · Cited by 125 · cited 1×
“Because of these attractive properties, MIL has been increasingly used in many other application fields over the last 20 years, such as image and video classification [4, 5, 6, 7, 8, 9], document 30”
Method
Keywords to visual categories: Multiple-instance learning forweakly supervised object categorization
Vijayanarasimhan, S., & Grauman, K. L. (2008) · 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.2008.4587632 · Cited by 123
Computer-aided diagnosis from weak supervision: A benchmarking study
Kandemir, M., & Hamprecht, F. A. (2014) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2014.11.010 · Cited by 123
Multiple Instance Learning for Computer Aided Diagnosis
Fung, G., Dündar, M., Krishnapuram, B., & Rao, R. B. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0058 · Cited by 118 · cited 2×
“Computational Complexity It has been noted by several authors that many MIL algorithms are too computationally expensive to be used with large data sets [179, 15].”
BackgroundMethod
A Note on Learning from Multiple-Instance Examples
Blum, A. L., & Kalai, A. T. (1998) · Machine Learning · doi:10.1023/a:1007402410823 · Cited by 115
Neural Networks for Multi-Instance Learning
Zhou, Z., & Zhang, M.-L. (2002) · Cited by 114
Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control
Fuduli, A., Gaudioso, M., & Giallombardo, G. (2004) · SIAM Journal on Optimization · doi:10.1137/s1052623402411459 · Cited by 108 · cited 1×
“this problem, in [46], a bundle algorithm [183] is used to solve the SVM optimization problem in linear time (O(n)).”
Method
Boosting for Multi-Graph Classification
Wu, J. X., Pan, S., Xingquan Zhu, A., & Cai, Z. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2327111 · Cited by 108 · cited 1×
“In [78, 100], complex objects such as web pages and scientific 680”
Background
Proceedings of the European Conference on Machine Learning (ECML)
Clark, A., Florêncio, C. C., & Watkins, C. (2006) · Cited by 107
Machine Hearing: An Emerging Field [Exploratory DSP
Lyon, R. F. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.937498 · Cited by 107 · cited 1×
“Some methods represent audio signals as spectrograms and use image recognition techniques to perform recognition [134].”
Method
Improve Multi-Instance Neural Networks through Feature Selection
Zhang, M.-L., & Zhou, Z. (2004) · Neural Processing Letters · doi:10.1023/b:nepl.0000016836.03614.9f · Cited by 105
Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)
(2024) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611978032 · Cited by 98
A Convex Method for Locating Regions of Interest with Multi-instance Learning
Li, Y., Kwok, J. T.-Y., Tsang, I. W.-H., & Zhou, Z. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04174-7_2 · Cited by 97
A multiple-instance learning framework for diabetic retinopathy screening
Quellec, G., Lamard, M., Abràmoff, M. D., Decencière, É., Laÿ, B., Erginay, A., Cochener, B., & Cazuguel, G. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.06.003 · Cited by 97 · cited 2×
“Quantitative evaluation is performed in [53, 120, 122].”
Background
Multiple Instance Classification via Successive Linear Programming
Mangasarian, O. L., & Wild, E. W. (2007) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-007-9343-5 · Cited by 97
Multiple-Instance Learning For Music Information Retrieval
Mandel, M., & Ellis, D. P. W. (2008) · doi:10.7916/d8086fpd · Cited by 93 · cited 1×
“In [133], the objective is to automatically determine the genre of musical excerpts.”
Background
2016 IEEE International Conference on Multimedia and Expo (ICME)
(2016) · doi:10.1109/icme36285.2016 · Cited by 93
Localized content based image retrieval
Rahmani, R., Goldman, S. A., Zhang, H., Krettek, J. E., & Fritts, J. E. (2005) · doi:10.1145/1101826.1101863 · Cited by 88 · cited 3×
“In this section, the reference methods with instance classification capabilities will be compared on three benchmark data sets: SIVAL, Birds and Newsgroups.”
Method
Music Emotion Recognition by Multi-label Multi-layer Multi-instance Multi-view Learning
Wu, B., Zhong, E., Horner, A., & Yang, Q. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654904 · Cited by 88 · cited 1×
“This is analogous to multi-view MIL which has been studied in a few papers [209, 210, 211, 212].”
Result
Action Recognition with Actons
Zhu, J., Wang, B., Yang, X., Zhang, W., & Tu, Z. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.442 · Cited by 88 · cited 2×
“A method based on multiple instance clustering was also proposed to discover a set of actons (sub-actions) from videos to create a mid-level representation of actions [118].”
BackgroundMethod
Deterministic Annealing for Multiple-Instance Learning
Gehler, P., & Chapelle, O. (2007) · Cited by 86
Systematically Differentiating Functions for Alternatively Spliced Isoforms through Integrating RNA-seq Data
Eksi, R., Li, H.-D., Menon, R., Wen, Y., Omenn, G. S., Kretzler, M., & Guan, Y. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003314 · Cited by 86 · cited 1×
“MIL has been used, among others, to predict a drug’s bioavailability [42], predict the binding affinity of peptides to major histocompatibility complex molecules [41], discover binding sites governing gene expression [96, 97] and predict gene functions [98].”
Method
Simultaneous Learning and Alignment: Multi-Instance and Multi-Pose Learning ?
Babenko, B., Tu, Z., & Belongie, S. (2008) · Cited by 86
Multiple instance learning for soft bags via top instances
Li, W., & Vasconcelos, N. M. (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.2015.7299056 · Cited by 83 · cited 2×
“For example, in computer vision applications, it is difficult to guarantee that negative images contain no positive patches: An image showing a house may contain flowers, but is unlikely to be annotated as a flower image [92].”
BackgroundMethod
Combining the Right Features for Complex Event Recognition
Tang, K., Yao, B., Fei-Fei, L., & Koller, D. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.335 · Cited by 83 · cited 1×
“Alternatively, events in videos can be represented, among others, by frame, texture and motion descriptors [167, 168].”
Background
A Boosting Approach to Multiple Instance Learning
Auer, P. L., & Ortner, R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30115-8_9 · Cited by 78
MILD: Multiple-Instance Learning via Disambiguation
Li, W.-J., & Yeung, D. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.58 · Cited by 77
MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets
Bandyopadhyay, S., Ghosh, D., Mitra, R., & Zhao, Z. (2015) · Scientific Reports · doi:10.1038/srep08004 · Cited by 74 · cited 1×
“affinity of peptides to major histocompatibility complex molecules [45], discover binding sites governing gene expression [119, 120] and predict gene functions [121].”
Background
Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis
Pappas, N., & Popescu-Belis, A. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1052 · Cited by 74 · cited 2×
“Alternatively, instances can be sentences [40,125], passages [6,126] or paragraphs [18].”
BackgroundMethod
Detector discovery in the wild: Joint multiple instance and representation learning
Hoffman, J., Pathak, D., Darrell, T., & Saenko, 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.2015.7298906 · Cited by 73 · cited 1×
“In several methods for object localization, bags are composed of many candidate bounding boxes corresponding to instances [1, 59, 130, 131, 132].”
Method
Fast Bundle Algorithm for Multiple-Instance Learning
Bergeron, C., Moore, G., Zaretzki, J. M., Breneman, C. M., & Bennett, K. P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.194 · Cited by 73 · cited 2×
“Ranking can be performed at the bag level [42] or at the instance level [43].”
BackgroundMethod
A theoretical and empirical analysis of support vector machine methods for multiple-instance classification
Doran, G. B., & Ray, S. (2013) · Machine Learning · doi:10.1007/s10994-013-5429-5 · Cited by 72
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
Jorgensen, Z., Zhou, Y., & Inge, M. (2008) · Cited by 69
Multiple instance learning of real valued data
Dooly, D. R., Zhang, Q., Goldman, S. A., & Amar, R. (2003) · doi:10.5555/944919.944949 · Cited by 68
Scalable Multi-instance Learning
Wei, X.-S., Wu, J., & Zhou, Z. (2014) · Proceedings · doi:10.1109/icdm.2014.16 · Cited by 67 · 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.”
Method
Multiple-instance ranking: Learning to rank images for image retrieval
Hu, Y., Li, M., & Yu, N. (2008) · 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.2008.4587352 · Cited by 66 · cited 1×
“Ranking can be performed at the bag level [42] or at the instance level [43].”
Background
Learning from Positive and Unlabeled Examples: A Survey
Zhang, B., & Zuo, W. (2008) · doi:10.1109/isip.2008.79 · Cited by 66 · cited 1×
“This type of problem is well studied in single-instance learning [188], 2010”
Background
Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs
Kandemir, M., Zhang, C., & Hamprecht, F. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10470-6_29 · Cited by 65
Positive and Unlabeled Multi-Graph Learning
Wu, J. X., Pan, S., Xingquan Zhu, A., Zhang, C., & Wu, X. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2527239 · Cited by 65
Multi-modal image annotation with multi-instance multi-label LDA
Nguyen, C.-T., Zhan, D., & Zhou, Z. (2013) · Cited by 62
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)
Chandler, J., & Richard, B. P. (2016) · Cited by 61
On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis
Melendez, J., van Ginneken, B., Maduskar, P., Philipsen, R. H. H. M., Ayles, H., & Sá‎nchez, C. I. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2505672 · Cited by 61
Robust multiple-instance learning ensembles using random subspace instance selection
Carbonneau, M.-A., Granger, E., Raymond, A. J., & Gagnon, G. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.035 · Cited by 60
Self-citation
Learning Discriminative Space–Time Action Parts from Weakly Labelled Videos
Sapienza, M., Fabio Cuzzolin, A., & Torr, P. H. S. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0662-8 · Cited by 58 · cited 1×
“In several methods for object localization, bags are composed of many candidate bounding boxes corresponding to instances [1, 59, 130, 131, 132].”
Method
Sparse coding and classifier ensemble based multi-instance learning for image categorization
Xiang-fa, S., Jiao, L., Yang, S., Zhang, X., & Shang, F. (2012) · Signal Processing · doi:10.1016/j.sigpro.2012.07.029 · Cited by 56
Interactive localized content based image retrieval with multiple-instance active learning
Zhang, D., Wang, F., Shi, Z., & Zhang, C. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.03.002 · Cited by 55 · cited 1×
“Existing literature on this subject is rather limited [149, 197, 198, 199].”
Background
Adaptive p-posterior mixture-model kernels for multiple instance learning
Wang, H., Yang, Q., & Zha, H. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390299 · Cited by 55 · cited 2×
“Many of these methods, like BoW [70, 94], miFV [72], FAMER [95] or PPMM [96] represent bags as instance distribu645”
Method
Concurrent Multiple Instance Learning for Image Categorization
Qi, G.-J., Hua, X., Rui, Y., Mei, T., Tang, J., & Zhang, H.-J. (2007) · 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.2007.383152 · Cited by 49 · cited 1×
“This has also been directly modeled in a tensor model [73] and in a multi-label framework [74].”
Method
Classification of COPD with Multiple Instance Learning
Cheplygina, V., Sorensen, L., Tax, D. M. J., Pedersen, J. H., Loog, M., & de Bruijne, M. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.268 · Cited by 48
Self-citation
Convex Multiple-Instance Learning by Estimating Likelihood Ratio
Li, F., & Sminchisescu, C. (2010) · Cited by 47
Multiple-instance learning as a classifier combining problem
Li, Y., Tax, D. M. J., Duin, R. P. W., & Loog, M. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.08.018 · Cited by 45
A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine
Zhang, C., Chen, X., Chen, M., Chen, S., & Shyu, M. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2005.1521628 · Cited by 45
Multiple instance ranking
Bergeron, C., Zaretzki, J. M., Breneman, C. M., & Bennett, K. P. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390163 · Cited by 44 · cited 1×
“Thus, many methods using SVM and SVM-like algorithms [4, 6, 27, 179, 181, 182] rapidly become impractical as the number of instances increases [46].”
Method
A Multi-scale Multiple Instance Video Description NetworkPreprint
Xu, H., Venugopalan, S., Ramanishka, V., Rohrbach, M., & Saenko, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05914 · Cited by 43
Multi-instance Multi-graph Dual Embedding Learning
Wu, J. X., Xingquan Zhu, A., Chengqi Zhang, B., & Cai, Z. (2013) · Proceedings · doi:10.1109/icdm.2013.121 · Cited by 42 · cited 1×
“This is analogous to multi-view MIL which has been studied in a few papers [209, 210, 211, 212].”
Result
Learning from multi-topic web documents for contextual advertisement
Zhang, Y., Surendran, A. C., Platt, J. C., & Narasimhan, M. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1402015 · Cited by 40 · cited 3×
“Alternatively, instances can be sentences [40,125], passages [6,126] or paragraphs [18].”
BackgroundMethod
An empirical study on image bag generators for multi-instance learning
Wei, X.-S., & Zhou, Z. (2016) · Machine Learning · doi:10.1007/s10994-016-5560-1 · Cited by 36 · cited 3×
“• When it is necessary to model combinations of instances to infer bag labels, bag-level and embedding methods perform better [15, 21, 49].”
BackgroundInfluential
Multiple Instance Learning on Structured Data
Zhang, D., Liu, Y., Si, L., Zhang, J., & Lawrence, R. D. (2011) · Cited by 36
Multiple Instance Learning of Pulmonary Embolism Detection with Geodesic Distance along Vascular Structure
Bi, J., & Liang, J. (2007) · 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.2007.383141 · Cited by 36
Efficient multi-instance learning for activity recognition from time series data using an auto-regressive hidden Markov model
Guan, X., Raich, R., & Wong, W. (2016) · International Conference on Machine Learning · Cited by 36
Single- vs. multiple-instance classification
Alpaydın, E., Cheplygina, V., Loog, M., & Tax, D. M. J. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.006 · Cited by 35
Self-citation
ReliefF-MI: An extension of ReliefF to multiple instance learning
Zafra, A., Pechenizkiy, M., & Ventura, S. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.03.052 · Cited by 35
Confidence-Rated Multiple Instance Boosting for Object Detection
Ali, K., & Saenko, K. (2014) · 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.2014.312 · Cited by 34 · cited 1×
“Other methods, like CR-MILBoost [66] and RSIS [30], estimate the probability that each instance is positive before training an ensemble of classifiers.”
Method
Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering
Zhang, D., Wang, F., Si, L., & Li, T. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2109011 · Cited by 33
Horror video scene recognition via Multiple-Instance learning
Wang, J., Li, B., Hu, W., & Wu, O. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946656 · Cited by 33 · cited 1×
“This problem formulation is also used in [114] to recognize scenes that are inappropriate for children.”
Background
Instance-level accuracy versus bag-level accuracy in multi-instance learning
Vanwinckelen, G., Tragante, V., Daan Fierens, A., & Blockeel, H. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-015-0416-z · Cited by 33 · cited 9×
“• The performance of MIL algorithms depends on several properties of the data set [15,18,20,21,23,53] .”
BackgroundMethodInfluential
Predicting MHC-II Binding Affinity Using Multiple Instance Regression
EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.94 · Cited by 32 · cited 2×
“Alternatively, on can simply replace a bag-level classifier by a regressor [45].”
Background
Label Stability in Multiple Instance Learning
Cheplygina, V., Sørensen, L., Tax, D. M. J., de Bruijne, M., & Loog, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_66 · Cited by 32 · cited 1×
“However, challenges and conclusions related to problem characteristics are also applicable to the other tasks.”
BackgroundSelf-citation
On classification with bags, groups and sets
Cheplygina, V., Tax, D. M. J., & Loog, M. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.03.008 · Cited by 31
Self-citation
Feature Learning from Spectrograms for Assessment of Personality Traits
Carbonneau, M., Granger, É., Attabi, Y., & Gagnon, G. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2763132 · Cited by 31 · cited 1×
“In [136], personality traits are inferred from speech signals represented as spectrograms in a BoW framework.”
Self-citation
G3P-MI: A genetic programming algorithm for multiple instance learning
Zafra, A., & Ventura, S. (2010) · Information Sciences · doi:10.1016/j.ins.2010.07.031 · Cited by 30 · cited 2×
“Finally, parallelization can be employed to reduce computation time, like in [187], where a parallelized version of the G3P-MI [83] algorithm have been proposed to leverage the power of GPUs, and thus deal with large quantities of 1965”
Method
Aerosol Optical Depth Prediction from Satellite Observations by Multiple Instance Regression
Wang, Z., Radosavljević, V., Han, B., Zoran Obradovic, A., & Vučetić, S. (2008) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972788.15 · Cited by 30 · cited 1×
“In this survey, the collective assumption designates all assumptions in which more than one instance defines bag labels.”
Background
MI2LS
Zhang, D., He, J., & Lawrence, R. D. (2013) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487651 · Cited by 29 · cited 1×
“Alternatively, instances can be sentences [40,125], passages [6,126] or paragraphs [18].”
Background
Instance-level semisupervised multiple instance learning
Jia, Y., & Zhang, C. (2008) · Cited by 29
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
McGovern, A., & Jensen, D. D. (2003) · doi:10.21236/ada465314 · Cited by 27 · cited 4×
“This is an important topic that has been addressed in some BoW methods, but was never thoroughly studied in other types of MIL methods, except for some methods using graphs [10, 26, 77, 78, 99].”
BackgroundMethodInfluential
Salience Assignment for Multiple-Instance Regression
Wagstaff, K. L., & Lane, T. D. R. (2007) · Cited by 26
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Zafra, A., Pechenizkiy, M., & Ventura, S. (2011) · Information Sciences · doi:10.1016/j.ins.2011.01.034 · Cited by 26
Ensemble one-class support vector machines for content-based image retrieval
Wu, R.-S., & Chung, W.-H. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.05.037 · Cited by 25
Locating Regions of Interest in CBIR with Multi-instance Learning Techniques
Zhou, Z., Xue, X., & Jiang, Y. Y. (2005) · Lecture notes in computer science · doi:10.1007/11589990_12 · Cited by 24 · cited 2×
“adapted in [66] to perform instance classification.”
Background
Multi-Instance Learning with Any Hypothesis ClassPreprint
SabatoSivan, & TishbyNaftali (2011) · arXiv (Cornell University) · doi:10.5555/2503308.2503338 · Cited by 24 · cited 2×
“Sabato and Tishby [47] analyzed the of sample complexity in MIL, and they found that the statistical performance of MIL is only mildly dependent on the number of instances per bag.”
Background
Speeding up multiple instance learning classification rules on GPUs
Cano, A., Zafra, A., & Ventura, S. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-014-0752-0 · Cited by 23 · cited 1×
“Finally, parallelization can be employed to reduce computation time, like in [187], where a parallelized version of the G3P-MI [83] algorithm have been proposed to leverage the power of GPUs, and thus deal with large quantities of 1965”
Method
The Detection of Concept Frames Using Clustering Multi-instance Learning
Tax, D. M. J., Hendriks, E. A., Valstar, M., & Pantić, M. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.715 · Cited by 23 · cited 3×
“Spatial dependency in images have also been modeled in with CRF in [74, 82].”
Method
A co-training framework for visual tracking with multiple instance learning
Lu, H., Zhou, Q.-H., Wang, D., & Xiang, R. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771455 · Cited by 22 · cited 1×
“Also in videos, MIL methods were proposed to perform object tracking [61, 138, 139].”
Method
Multi-Instance Dimensionality Reduction
Sun, Y., Ng, M. K., & Zhou, Z. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7700 · Cited by 20
Deep Multi-Instance Transfer LearningPreprint
Kotzias, D., Denil, M., Blunsom, P., & de Freitas, N. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.3128 · Cited by 19 · cited 1×
“In [40, 125, 129, 130], MIL was used to infer the sentiment expressed in individual sentences based on the labels provided for entire user reviews.”
Method
A Discriminative Data-Dependent Mixture-Model Approach for Multiple Instance Learning in Image Classification
Wang, Q., Si, L., & Zhang, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_47 · Cited by 17 · cited 1×
“The mixturemodel in [110] naturally represents different positive clusters.”
Background
Multiple instance learning-based birdsong classification using unsupervised recording segmentation
Ruiz-Muñoz, J. F., Orozco‐Alzate, M., & Castellanos-Dominguez, G. (2015) · International Conference on Artificial Intelligence · Cited by 17
Weakly supervised scalable audio content analysis
Kumar, A., & Raj, B. (2016) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7552989 · Cited by 17
Multiple-instance learning from distributions
Doran, G. B., & Ray, S. (2016) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 17
Maximum margin multiple-instance feature weighting
Chai, J., Chen, H., Huang, L., & Shang, F. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2013.12.009 · Cited by 15 · cited 3×
“This type of data is often difficult to handle by classifiers using Euclidean-like distance measures.”
BackgroundMethodInfluential
Multiple instance learning for landmine detection using ground penetrating radar
Manandhar, A., Morton, K. D., Collins, L. M., & Torrione, P. A. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.917947 · Cited by 15 · cited 1×
“In [140,141], MIL classifiers detect buried landmines from ground-penetrating radar signals.”
Background
A multiple instance learning approach for landmine detection using Ground Penetrating Radar
Karem, A., & Frigui, H. (2011) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2011.6049271 · Cited by 15 · cited 1×
“In [140,141], MIL classifiers detect buried landmines from ground-penetrating radar signals.”
Background
Progressive Learning for Interactive Surveillance Scenes Retrieval
Meessen, J., Desurmont, X., Delaigle, J.-F., Vleeschouwer, C. D., & Macq, B. M. M. (2007) · 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.2007.383517 · Cited by 15 · cited 1×
“Existing literature on this subject is rather limited [149, 197, 198, 199].”
Background
Learning Curves for the Analysis of Multiple Instance Classifiers
Tax, D. M. J., & Duin, R. P. W. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89689-0_76 · Cited by 15 · cited 1×
“In APR [3] the region is a hyper-rectangle, while in many others it is one, or a collection of, hyper-spheres/-ellipses [29, 31, 33, 88].”
Background
Simpler Non-Parametric Methods Provide as Good or Better Results to Multiple-Instance Learning
Venkatesan, R., Chandakkar, P. S., & Li, B. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.299 · Cited by 14 · cited 3×
“[52] showed that simple lazy-learning techniques could be applied to some MIL problems to obtain results comparable to state-of-the-art techniques.”
BackgroundResultInfluential
Avoiding False Positive in Multi-Instance Learning
Han, Y., Tao, Q., & Wang, J. (2012) · Cited by 14
Characterizing Multiple Instance Datasets
Cheplygina, V., & Tax, D. M. J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24261-3_2 · Cited by 14 · cited 2×
“• The performance of MIL algorithms depends on several properties of the data set [15, 18, 20, 21, 23, 48].”
BackgroundSelf-citation
Multiple-Instance Learning with Instance Selection via Dominant Sets
Erdem, A., & Erdem, E. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24471-1_13 · Cited by 13 · cited 1×
“In [94], robustness to label noise is obtained by using dominant sets to perform clustering and select relevant instance prototype in a bag-embedding algorithm similar to MILES [4].”
Method
Multimedia Event Detection Using Event-Driven Multiple Instance Learning
Phan, S., Le, D.-D., & Satoh, S. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806330 · Cited by 13 · cited 2×
“Because of these attractive properties, MIL has been increasingly used in many other application fields over the last 20 years, such as image and video classification [4, 5, 6, 7, 8, 9], document 30”
Method
Non-I.I.D. Multi-Instance Dimensionality Reduction by Learning a Maximum Bag Margin Subspace
Ping, W., Xu, Y., Ren, K., Chi, C., & Shen, F. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7653 · Cited by 12
Multi-Instance Learning from Positive and Unlabeled Bags
Wu, J. X., Xingquan Zhu, A., Chengqi Zhang, B., & Cai, Z. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06608-0_20 · Cited by 12 · cited 1×
“So far, only a handful of papers are dedicated to this subject [189, 190, 191].”
Background
Multiple instance learning based on positive instance selection and bag structure construction
Li, Z., Geng, G., Feng, J. H., Peng, J., Wen, C., & Liang, J. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.11.013 · Cited by 11

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

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
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
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.”
Background
Artificial 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
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection
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 .”
Background
Federated 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

See all 740

Authors: Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G.
Affiliations: École de Technologie Supérieure (CA)
Erasmus MC (NL)
Corresponding author: Carbonneau, Marc-André
ORCID: Carbonneau, Marc-André
Cheplygina, Veronika
Gagnon, Ghyslain
Venue: Pattern Recognition
Publisher: Elsevier BV
Published: 2018-05
Volume: 77
Pages: 329-353
ISSN: 0031-3203 (ISSN-L)
DOI: 10.1016/j.patcog.2017.10.009
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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