Neurocomputing

In the research field of multimedia retrieval, unsupervised multi-modal hashing has received widespread attention because of its high retrieval efficiency, low storage cost and semantic label independence. However, there are still ...

Accurate high-definition maps with lane markings are often used as the navigation back-end for commercial autonomous vehicles. Currently, most high-definition maps are manually constructed by human labelling. Therefore, it is urgently ...

Named entity recognition (NER) is a fundamental problem in natural language processing. In particular, nested entities are commonly existed in real-life textual data for the NER task. However, the current span-based methods for nested ...

Deep learning is an effective tool to assist doctors with many time-consuming and error-prone medical image analytical tasks. However, deep models are shown to be vulnerable to adversarial attacks, posing significant challenges to ...

In this paper, the stability for a class fractional-order inertial neural networks with time-delay are investigated. Moreover, some sufficient conditions for the Mittag-Leffler stability and the asymptotical ω-periodicity are obtained, ...

As the main research content of artificial intelligence, the artificial neural network has been widely concerned because of its excellent performance in the fields such as computer vision and natural language processing since it was ...

• Instead of supervised methods, unsupervised adversarial learning is proposed to construct spectral mapping network for HS change detection, which not only ...

Unlike the existing change detection approaches based on the multispectral (MS) image and synthetic aperture radar (SAR) image datasets, a novel unsupervised hyperspectral change detection (UHCD) framework is proposed in this paper. ...

Vehicle Re-Identification (V-ReID) is a critical task that associates the same vehicle across images from different camera viewpoints. Many works explore attribute clues to enhance V-ReID; however, there is usually a lack of effective ...

Under the Joint Photographic Experts Group (JPEG) framework, this paper proposes a simple yet efficient focus measure (FM) in discrete cosine transform (DCT) domain for fast multi-focus image fusion. At first, the DCT coefficients of ...

The deep learning era endows immense opportunities for ubiquitous robotic applications by leveraging big data generated from widespread sensors and ever-growing computing capability. While the growing demands for natural human-robot ...

We propose a novel framework to transfer the portrait image into its correspondence with photo-realistic and cartoon style. The existing work on neural style transfer conducts impressive results on artistic style transfer; however, the ...

Fuzzy rough set is a theoretical framework of fuzzy uncertainty management, and discernibility matrix offers a mathematical foundation for algorithm construction of feature learning. The approaches of fuzzy rough set and discernibility ...

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• We propose a SCAM that integrates the information from both modes to reduce the fusion ambiguity caused by unreliable depth maps.

Effective exploration of useful contextual information in multi-modal images is an essential task in salient object detection. Nevertheless, the existing methods based on the early-fusion or the late-fusion schemes cannot address this ...

Adaptive response to the timely constant stimulus is the common feature of real neurons. The circuit of the adaptive neuron model consumes less power and requires less data transmission bandwidth compared to the circuit of the non-...

This article introduces a kind of aperiodically sampled-data intermittent control to investigate the synchronization issue of stochastic multi-links network. Therein, the sampling intervals are variable in the aperiodically ...

In person search task, it is hard to retrieve the query persons undergoing large visual changes. To tackle this problem, we propose to exploit the context information to rectify the original individual similarity for better retrieval. ...

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• We present a novel HSAN model for aspect identification task.
• Compared with ...

Aspect identification is one of the important sub-tasks in opinion mining and this task can be considered as a token-level sequencing problem. Most recent approaches employ BERT based network to identify the aspect term, which is often ...

Inferring the intended actions of road-sharing users with autonomous ground vehicles in particularly vulnerable ones like cyclists is considered one of the tough tasks facing the wide-spread deployment of autonomous ground vehicles. ...

Synthetic aperture radar (SAR) image recognition is an important stage of SAR image interpretation. The standard convolutional neural network (CNN) has been successfully applied in the SAR image recognition due to its powerful feature ...

Video engagement is important in online advertisements where there is no physical interaction with the consumer. Engagement can be directly measured as the number of seconds after which a consumer skips an advertisement. In this paper, ...

Image super-resolution from low-resolution fundus image has valuable applications in clinical practices. The popular methods yield unsatisfactory results when the fundus images are contaminated due to the bleeding or plaques caused by ...

Dialogue systems have made massive promising progress contributed by deep learning techniques and have been widely applied in our life. However, existing end-to-end neural models suffer from the problem of tending to generate ...

• The paper provides a robust regression method for interval-valued variables.
• ...

Interval-valued data are needed to manage either the uncertainty related to measurements, or the variability inherent to the description of complex objects representing group of individuals. A number of regression methods suitable to ...

• We mathematically proved that several spectral alignment methods (NMF, IsoRank and FINAL) can be unified into a general heuristic form. From representation ...

Graph alignment, also known as network alignment has many applications in data mining tasks. It aims to find the node correspondence across disjoint graphs. Recently, various methods like representation learning methods, spectral ...

In this paper we propose a framework using multi-channel convolutional neural network (MC–CNN) for recognizing the grammatical class (verb or noun) of covertly-spoken words from electroencephalogram (EEG) signals. Our proposed network ...

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Optimization of discrete structures aims at generating a new structure with the better property given an existing one, which is a fundamental problem in machine learning. Different from the continuous optimization, the realistic ...

Deep learning has achieved a remarkable success in areas such as brain-computer interface systems (BCI). However, electroencephalography (EEG) signals evoked by motor imagery (MI) are sometimes limited in their amount due to invalid ...

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• A novel and simple geometric analysis network is proposed using heatmaps.
• A FCN-...

In this paper, a traffic scene construction framework is proposed based on geometric and semantic analysis of road image sequences using convolutional neural networks (CNNs). For geometric analysis branch of the framework, the image ...

As neuron is the fundamental unit of the nervous system, it is one of the main building blocks in the spiking neural networks hardware implementation. To implement hardware that consists of many thousands of neurons and accurately ...