Object Detection using Deep Learning Algorithm CNN

Deep CNN Ensemble with Data Augmentation for Object Detection

We report on the methods used in our recent DeepEnsembleCoco submission to the PASCAL VOC 2012 challenge, which achieves state-of-theart performance on the object detection task. Our method is a variant of the R-CNN model proposed by Girshick et al. [4] with two key improvements to training and evaluation. First, our method constructs an ensemble of deep CNN models with different architectures ...

Concept drift detection in business process logs using deep learning

Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...

Object Detection for Autonomous Driving Using Deep Learning

ME R-CNN: Multi-Expert R-CNN for Object Detection

Recent CNN-based object detection methods have drastically improved their performances but still use a single classifier as opposed to ”multiple experts” in categorizing objects. The main motivation of introducing multi-experts is twofold: i) to allow different experts to specialize in different fundamental object shape priors and ii) to better capture the appearance variations caused by differ...

Deep learning for class-generic object detection

We investigate the use of deep neural networks for the novel task of class-generic object detection. We show that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided. In addition, we show that bounding box labels yield a 1% performance increase o...