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Found 12 papers, 6 papers with code
Counting Network for Learning from Majority Label
Existing MIL methods are unsuitable for LML due to aggregating confidences, which may lead to inconsistency between the bag-level label and the label obtained by counting the number of instances for each class.
Boosting for Bounding the Worst-class Error
This paper tackles the problem of the worst-class error rate, instead of the standard error rate averaged over all classes.
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
In this paper, we propose a bag-level data augmentation method for LLP called MixBag, based on the key observation from our preliminary experiments; that the instance-level classification accuracy improves as the number of labeled bags increases even though the total number of instances is fixed.
Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization
This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags.
Optimal Rejection Function Meets Character Recognition Tasks
The highlights of LwR are: (1) the rejection strategy is not heuristic but has a strong background from a machine learning theory, and (2) the rejection function can be trained on an arbitrary feature space which is different from the feature space for classification.
Revealing Reliable Signatures by Learning Top-Rank Pairs
Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields.
Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification
Our proposed method takes a pseudo labeling approach for cell detection from imperfect annotated data.
AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee
For visual object tracking, it is difficult to realize an almighty online tracker due to the huge variations of target appearance depending on an image sequence.
Ranked #1 on
Visual Object Tracking
on TempleColor128
Theory and Algorithms for Shapelet-based Multiple-Instance Learning
We propose a new formulation of Multiple-Instance Learning (MIL), in which a unit of data consists of a set of instances called a bag.
Simplified and Unified Analysis of Various Learning Problems by Reduction to Multiple-Instance Learning
In this work, we focus on a particular problem formulation called Multiple-Instance Learning (MIL), and show that various learning problems including all the problems mentioned above with some of new problems can be reduced to MIL with theoretically guaranteed generalization bounds, where the reductions are established under a new reduction scheme we provide as a by-product.
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers
Classifiers based on a single shapelet are not sufficiently strong for certain applications.
Boosting the kernelized shapelets: Theory and algorithms for local features
We consider binary classification problems using local features of objects.
