Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing detection systems, which are able to automatically detect and block fraudulent transactions. From machine learning perspective, the task of detecting transactions binary classification problem. Classification models commonly trained evalu...

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware their instance labels. To this end, we propose a new pipeline for end-to-end segmentation (SIS) that predicts class-agnostic mask each detected instance. better use rich feature hierarchies in deep networks and enhance side predictions, regularized dense connect...

Abstract Multiple instance boosting (MILBoost) is a framework which uses multiple learning (MIL) with technique to solve the problems regarding weakly labeled inexact data. This paper proposes an enhanced framework—evolutionary MILBoost (EMILBoost) utilizes differential evolution (DE) optimize combination of weak classifier or estimator weights in framework. A standard MIL dataset MUSK and bina...

Instance segmentation in aerial images is of great significance for remote sensing applications, and it inherently more challenging because cluttered background, extremely dense small objects, objects with arbitrary orientations. Besides, current mainstream CNN-based methods often suffer from the trade-off between labeling cost performance. To address these problems, we present a pipeline hybri...

3D object detection is a critical task in autonomous driving. Recently multi-modal fusion-based methods, which combine the complementary advantages of LiDAR and camera, have shown great performance improvements over mono-modal methods. However, so far, no methods attempted to utilize instance-level contextual image semantics guide detection. In this paper, we propose simple effective Painting A...

We live in a world that is being driven by data. This leads to challenges of extracting and analyzing knowledge from large volumes An example such challenge intrusion detection. Intrusion detection data sets are characterized huge volumes, which affects the learning classifier. So there need reduce size training sets. Fortunately, inspection analysis available showed many instances very similar...