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State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that(More)
Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them. In the context of deep neural networks, this idea is often realized by handdesigned network architectures with layers that are shared across tasks and branches that encode task-specific features. However, the(More)
Efficient generation of high-quality object proposals is an essential step in state-of-the-art object detection systems based on deep convolutional neural networks (DCNN) features. Current object proposal algorithms are computationally inefficient in processing high resolution images containing small objects, which makes them the bottleneck in object(More)
In this paper a new electrochemical DNA biosensor was prepared by using graphene (GR) and nickel oxide (NiO) nanocomposite modified carbon ionic liquid electrode (CILE) as the substrate electrode. GR and NiO nanoparticles were electrodeposited on the CILE surface step-by-step to get the nanocomposite. Due to the strong affinity of NiO with phosphate groups(More)
Feature pooling layers (e.g., max pooling) in convolutional neural networks (CNNs) serve the dual purpose of providing increasingly abstract representations as well as yielding computational savings in subsequent convolutional layers. We view the pooling operation in CNNs as a twostep procedure: first, a pooling window (e.g., 2 ˆ 2) slides over the feature(More)
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