Neurocomputing

We present a method based on a robust two-layer cascade reciprocal pipeline (TCPR) and the context-aware dissimilarity measure (CADM) for feature-based image registration. We first build the two-layer pipeline to find the optimal ...

• SSNet (Structure-Semantic Net) generates target typography of high image quality, preserving structure and semantics of Chinese characters.

The abundant complex Chinese characters often lead to the high cost of time and labor in its typography production, which cannot meet various demands of typohraphies in daily life. Image translation methods are becoming the mainstream ...

Multi-modal foreground detection, which integrates multiple complementary data like visible and thermal infrared sources for moving object detection, has received more and more attention recently. In this paper, we propose a novel M...

Neural networks lately have achieved a great success on sentiment classification due to their ability of feature extraction. However, it remains as an enormous challenge to model long texts in document-level sentiment classification as ...

• Proposed the infimal convolution smoothing technique to approximate the non-differentiable loss function.

This paper addresses the robust sparse recovery problem in the presence of impulsive measurement noise. In order to overcome the poor performance of ℓ₂-norm loss function with the outliers under the impulsive noise, we employ the ℓ₁-...

In order to process multi-view multi-label data sets, we propose global and local multi-view multi-label learning (GLMVML). This method can exploit global and local label correlations of both the whole data set and each view ...

This paper considers the coordination motion control of multiple robot manipulators by developing a unified framework of super-twisting zeroing neural network (ST-ZNN), and proposes a novel external disturbances suppression model. The ...

Hardware implementations of spiking neural networks in portable devices can improve many applications of robotics, neurorobotics or prosthetic fields in terms of power consumption, high-speed processing and learning mechanisms. Analog ...

Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of ...

The Wasserstein distance originated from the optimal transport theory is a general and flexible statistical metric in a variety of image processing problems. In this paper, we propose a novel Wasserstein driven low-dimensional manifold ...

On purpose of aiding detection and recognition for railway infrastructure and dramatic changes in the environment around railways, visual inspection based on unmanned aerial vehicle (UAV) images is a highlight. However, UAV images ...

Convolutional neural networks have been successfully applied to detect salient objects in an image. However, how to better use convolutional features for co-saliency detection, which is an emerging branch of saliency detection, is not ...

Image saliency and co-saliency detection that aim to detect salient objects in an image or common salient objects in a group of images are import in computer vision. Researchers often treat saliency and co-saliency as two separate ...

Answer selection is one of the most important techniques in question answering applications since it can improve the user experience to a large extend. To achieve a better answer selection performance, a fundamental approach is to ...

• We propose APLNet using multiple stages for progressive detection to improve the performance of single-stage detectors. In each stage, only a convolutional ...

Single-stage detectors depend on a simple regression network to predict category scores and regress box offsets for a fixed set of default boxes directly. The regression network needs to have high generalization capability, so as to ...

• We propose Jumper, a novel framework that models text classification as a sequential decision process.

Text classification is one of the key problems in natural language processing (NLP), and in early years, it was usually accomplished by feature-based machine learning models. Recently, the deep neural network has become a powerful ...

Anomaly detection in crowded scenes plays a crucial role in automatic video surveillance to avert any casualty in the areas witnessing the high amount of footfalls. The key challenge for automatically classifying the anomalies in crowd ...