Natural and man-made disasters cause huge damage to built infrastructures and results in loss of human lives.
In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
We used the LINEMOD dataset to evaluate the proposed method and the experimental results show that the proposed method reduces the memory requirement almost 99\% in comparison to the original architecture reducing half the accuracy in one of the metrics.
The hard assignments of closest point correspondences based on spatial distances are sensitive to the initial rigid transformation and noisy/outlier points, which often cause ICP to converge to wrong local minima.
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input.
On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process.
We establish a benchmark suite consisting of different types of PDF document datasets that can be utilized for cross-domain DOD model training and evaluation.
We study a decentralized channel allocation problem in an ad-hoc Internet of Things (IoT) network underlaying on a spectrum licensed to an existing wireless network.
In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity.
Learning a good image prior is a long-term goal for image restoration and manipulation.