Understanding an image goes beyond recognizing and locating the objects in it,the relationships between objects also very important in image understanding.Most previous methods have focused on recognizing local predic...Understanding an image goes beyond recognizing and locating the objects in it,the relationships between objects also very important in image understanding.Most previous methods have focused on recognizing local predictions of the relationships.But real-world image relationships often determined by the surrounding objects and other contextual information.In this work,we employ this insight to propose a novel framework to deal with the problem of visual relationship detection.The core of the framework is a relationship inference network,which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the image.Experimental results on Stanford VRD and Visual Genome demonstrate that the proposed method achieves a good performance both in efficiency and accuracy.Finally,we demonstrate the value of visual relationship on two computer vision tasks:image retrieval and scene graph generation.展开更多
随着移动设备和社交软件的普遍应用,下一个兴趣点推荐(next POI recommendation)变成了基于位置的社交网络(LBSN)的一个非常重要的任务。现实生活中用户访问的下一个兴趣点通常受到用户签到序列信息、用户关系和该地点的上下文信息等诸...随着移动设备和社交软件的普遍应用,下一个兴趣点推荐(next POI recommendation)变成了基于位置的社交网络(LBSN)的一个非常重要的任务。现实生活中用户访问的下一个兴趣点通常受到用户签到序列信息、用户关系和该地点的上下文信息等诸多方面的影响。基于循环神经网络(RNN)的方法已经被广泛的应用到下一个兴趣点推荐中,但是这些基于RNN的方法缺乏对用户关系进行深入建模。为了解决上述问题,提出了一种整合用户关系和门控循环单元(GRU)进行下一个兴趣点推荐的模型(GRU-R),同时该模型能够考虑用户签到序列信息、用户关系、兴趣点的时空信息和类别信息等进行下一个兴趣点推荐。在两个真实公开的数据集上进行实验,结果表明提出的模型比现有主流的下一个兴趣点推荐算法具有更高的推荐准确性。展开更多
Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consum...Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration.A single-step application of machine learning(ML)is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy.The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approach-This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars.The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data,and the second step detects abnormal fuel economy in relation to contextual information.Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model.The contextual anomaly is detected by following two approaches,kernel quantile estimator and one-class support vector machine.The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour.Any error beyond a threshold is classified as an anomaly.The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection.The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder,and the performance of both models is compared.The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.Findings-A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder.Both models achieve prediction accuracy within a range of 98%-100%for prediction as a first step.Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%-100%,whereas the one-class support vectormachine approach performs within the range of 99.3%-100%.Research limitations/implications-The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns.However,it can be extended to correlate driver’s physiological state such as fatigue,sleep and stress to correlate with driving behaviour and fuel economy.The anomaly detection approach here is limited to providing feedback to driver,it can be extended to give contextual feedback to the steering controller or throttle controller.In the future,a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implications-The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts.It can also be used as a training tool for improving driving efficiency for new drivers.It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/value-This paper contributes to the existing literature by providing anMLpipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values.The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy.The main contributions for this approach are as follows:(1)a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption.(2)Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.展开更多
文摘Understanding an image goes beyond recognizing and locating the objects in it,the relationships between objects also very important in image understanding.Most previous methods have focused on recognizing local predictions of the relationships.But real-world image relationships often determined by the surrounding objects and other contextual information.In this work,we employ this insight to propose a novel framework to deal with the problem of visual relationship detection.The core of the framework is a relationship inference network,which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the image.Experimental results on Stanford VRD and Visual Genome demonstrate that the proposed method achieves a good performance both in efficiency and accuracy.Finally,we demonstrate the value of visual relationship on two computer vision tasks:image retrieval and scene graph generation.
文摘随着移动设备和社交软件的普遍应用,下一个兴趣点推荐(next POI recommendation)变成了基于位置的社交网络(LBSN)的一个非常重要的任务。现实生活中用户访问的下一个兴趣点通常受到用户签到序列信息、用户关系和该地点的上下文信息等诸多方面的影响。基于循环神经网络(RNN)的方法已经被广泛的应用到下一个兴趣点推荐中,但是这些基于RNN的方法缺乏对用户关系进行深入建模。为了解决上述问题,提出了一种整合用户关系和门控循环单元(GRU)进行下一个兴趣点推荐的模型(GRU-R),同时该模型能够考虑用户签到序列信息、用户关系、兴趣点的时空信息和类别信息等进行下一个兴趣点推荐。在两个真实公开的数据集上进行实验,结果表明提出的模型比现有主流的下一个兴趣点推荐算法具有更高的推荐准确性。
文摘Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration.A single-step application of machine learning(ML)is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy.The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approach-This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars.The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data,and the second step detects abnormal fuel economy in relation to contextual information.Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model.The contextual anomaly is detected by following two approaches,kernel quantile estimator and one-class support vector machine.The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour.Any error beyond a threshold is classified as an anomaly.The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection.The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder,and the performance of both models is compared.The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.Findings-A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder.Both models achieve prediction accuracy within a range of 98%-100%for prediction as a first step.Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%-100%,whereas the one-class support vectormachine approach performs within the range of 99.3%-100%.Research limitations/implications-The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns.However,it can be extended to correlate driver’s physiological state such as fatigue,sleep and stress to correlate with driving behaviour and fuel economy.The anomaly detection approach here is limited to providing feedback to driver,it can be extended to give contextual feedback to the steering controller or throttle controller.In the future,a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implications-The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts.It can also be used as a training tool for improving driving efficiency for new drivers.It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/value-This paper contributes to the existing literature by providing anMLpipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values.The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy.The main contributions for this approach are as follows:(1)a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption.(2)Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.
