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Robot Object Detection and Tracking Based on Image-Point Cloud Instance Matching. [PDF]

Sensors (Basel)
Wang H, Zhu R, Ye Z, Li Y.
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MultiScale hierarchical attention network for stain free breast cancer detection in microscopic hyperspectral imaging. [PDF]

Sci Rep
Chen Z, Yang Q, Qin G, Ma X, Lu Z, Li H, Su B.   +6 more
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Scalable Multi-instance Learning

2014 IEEE International Conference on Data Mining, 2014
Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects such as images and genes. However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an efficient and scalable MIL algorithm named miFV. Our algorithm
Xiu-Shen Wei, Jianxin Wu, Zhi-Hua Zhou
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Multi-instance Metric Learning

2011 IEEE 11th International Conference on Data Mining, 2011
Multi-instance learning, like other machine learning and data mining tasks, requires distance metrics. Although metric learning methods have been studied for many years, metric learners for multi-instance learning remain almost untouched. In this paper, we propose a framework called Multi-Instance MEtric Learning (MIMEL) to learn an appropriate ...
Ye Xu, Wei Ping, Andrew T. Campbell
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Scalable Algorithms for Multi-Instance Learning

IEEE Transactions on Neural Networks and Learning Systems, 2017
Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects, such as images and genes. However, most existing MIL algorithms can only handle small- or moderate-sized data. In order to deal with large-scale MIL problems, we propose MIL based on the vector of locally aggregated descriptors ...
Xiu-Shen, Wei, Jianxin, Wu, Zhi-Hua, Zhou   +2 more
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Multi-Instance Learning with Distribution Change

Proceedings of the AAAI Conference on Artificial Intelligence, 2014
Multi-instance learning deals with tasks where each example is a bag of instances, and the bag labels of training data are known whereas instance labels are unknown. Most previous studies on multi-instance learning assumed that the training and testing data are from the same distribution; however, this assumption is often violated in ...
Wei-Jia Zhang, Zhi-Hua Zhou
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Semisupervised, Multilabel, Multi-Instance Learning for Structured Data

Neural Computation, 2017
Many classification tasks require both labeling objects and determining label associations for parts of each object. Example applications include labeling segments of images or determining relevant parts of a text document when the training labels are available only at the image or document level. This task is usually referred to as multi-instance (MI)
Soleimani, Hossein, Miller, David J.
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Feature selection in multi-instance learning

Neural Computing and Applications, 2012
Multi-instance learning was first proposed by Dietterich et al. (Artificial Intelligence 89(1–2):31–71, 1997) when they were investigating the problem of drug activity prediction. Here, the training set is composed of labeled bags, each of which consists of many unlabeled instances.
Rui Gan, Jian Yin
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Unsupervised multi-instance learning for protein structure determination

Journal of Bioinformatics and Computational Biology, 2021
Many regions of the protein universe remain inaccessible by wet-laboratory or computational structure determination methods. A significant challenge in elucidating these dark regions in silico relates to the ability to discriminate relevant structure(s) among many structures/decoys computed for a protein of interest, a problem known as decoy selection.
Fardina Fathmiul, Alam, Amarda, Shehu
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