Multi-task multi-label multiple instance learning

For automatic object detection tasks,large amounts of training images are usually labeled to achieve more reliable training of the object classifiers;this is cost-expensive since it requires hiring professionals to label large-scale training images.When a large number of object classes come into view,the issue of obtaining a large enough amount of the labeled training images becomes more critical.There are three potential solutions to reduce the burden for image labeling:(1) allowing people to provide the object labels loosely at the image level rather than at the object level(e.g.,loosely-tagged images without identifying the exact object locations in the images) ;(2) harnessing large-scale collaboratively-tagged images that are available on the Internet;and,(3) developing new machine learning algorithms that can directly leverage large-scale collaboratively-or loosely-tagged images for achieving more eective training of a large number of object classifiers.Based on these observations,a multi-task multi-label multiple instance learning(MTML-MIL) algorithm is developed in this paper by leveraging both inter-object correlations and large-scale loosely-labeled images for object classifier training.By seamlessly integrating multi-task learning,multi-label learning,and multiple instance learning,our MTML-MIL algorithm can achieve more accurate training of a large number of inter-related object classifiers(where an object network is constructed for determining the inter-related learning tasks directly in the feature space rather than in the label space) .Our experimental results have shown that our MTML-MIL algorithm can achieve higher detection accuracy rates for automatic object detection.

PT-MIL:Parallel transformer based on multi-instance learning for osteoporosis detection in panoramic oral radiography

Osteoporosis is a systemic disease characterized by low bone mass,impaired bone microstruc-ture,increased bone fragility,and a higher risk of fractures.It commonly affects postmenopausal women and the elderly.Orthopantomography,also known as panoramic radiography,is a widely used imaging technique in dental examinations due to its low cost and easy accessibility.Previous studies have shown that the mandibular cortical index(MCI)derived from orthopantomography can serve as an important indicator of osteoporosis risk.To address this,this study proposes a parallel Transformer network based on multiple instance learning.By introducing parallel modules that alleviate optimization issues and integrating multiple-instance learning with the Transformer architecture,our model effectively extracts information from image patches.Our model achieves an accuracy of 86%and an AUC score of 0.963 on an osteoporosis dataset,which demonstrates its promising and competitive performance.

基于Paas模式下多租户感知BPEL引擎的研究

多租户感知工作流程引擎(Multi-Tenant Aware Workflow Engine)是针对目前工作流程引擎系统缺少租户对业务流程的灵活、动态的配置,导致企业信息系统间业务流程的缺乏可重复性使用的现状,提出了基于PaaS模式下多租户感知流程引擎的概念和模型,同时对多租户感知流程引擎的实现提出了总体要求,功能性要求以及非功能性要求,并通过对Or chestra流程引擎的扩展和延伸实现了多租户感知流程引擎的原型。

An Adaptive Classifier Based Approach for Crowd Anomaly Detection

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security.Intelligent video surveillance systems make extensive use of data mining,machine learning and deep learning methods.In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning.In this approach,Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes.We use multiple instance learning(MIL)to dynamically develop a deep anomalous ranking framework.This technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video sections.We use the multi-objective whale optimization algorithm to optimize the entire process and get the best results.The performance parameters such as accuracy,precision,recall,and F-score are considered to evaluate the proposed technique using the Python simulation tool.Our simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.

Robust visual tracking via randomly projected instance learning

Purpose–The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.Design/methodology/approach–This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework.First,the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling.Second,a coarse-to-fine search approach is first integrated into the framework of multiple instance learning(MIL)for less detections.Findings–The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.Originality/value–The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection.This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.