Wei Ke

Macao Polytechnic University · Faculty of Applied Sciences

The task of dense video captioning is to generate detailed natural-language descriptions for an original video, which requires deep analysis and mining of semantic captions to identify events in the video. Existing methods typically follow a localisation-then-captioning sequence within given frame sequences, resulting in caption generation that is...

Zebrafish behavioral patterns reveal valuable insights for biomedical research. To accurately identify these patterns, visual tracking systems need to reconstruct 3D trajectories from multi-view video sequences. However, 3D zebrafish tracking faces challenges such as the dynamics in movements, the similarity in appearances, and the distortion cause...

Dense video caption is a task that aims to help computers analyze the content of a video by generating abstract captions for a sequence of video frames. However, most of the existing methods only use visual features in the video and ignore the audio features that are also essential for understanding the video. In this paper, we propose a fusion mod...

In person re-identification (re-id), the key to retrieving the correct person image is to extract discriminative features. The features at different levels are considered complementary. In this work, we design a person re-id learning network that can extract mutually multi-level features called ASCLNet. ASCLNet contains three feature branches, and...

As a vital technology for ensuring the stable operation of industrial equipment, fault diagnosis has received a lot of research in recent years. Most complex industrial processes are in normal working conditions during operation, so the amount of data collected under normal working conditions is much larger than that under fault working conditions....

In actual industrial processes, although a large number of original data are easy to obtain, only a few samples are effectively labelled, which is insufficient to construct a supervised fault diagnostic model. Facing the industrial demand of fault diagnosis, in this paper, a novel density ratio (DR)‐based batch active learning (BAL) fault diagnosis...

Light field (LF) images acquired by hand-held devices suffer from a trade-off between spatial and angular resolutions. To solve this problem, super-resolution (SR) in the spatial and angular domains is studied separately in previous works. However, spatial-angular correlation can not be reconstructed effectively by the separate SR methods. In this...

Light field (LF) cameras record multiple perspectives through a sparse sampling of real scenes, and these perspectives provide each other with complementary information. This information is beneficial to the LF super-resolution (LFSR). Comparing with traditional single-image super-resolution (SISR), LF has the parallax structure and perspective cor...

Multiple Object Tracking (MOT) usually adopts the Tracking-by-Detection paradigm, which transforms the problem into data association. However, these methods are restricted by detector performance, especially in dense scenes. In this paper, we propose a novel group-guided data association, which improves the robustness of MOT to error detections and...

Light field (LF) images taken by plenoptic cameras can record spatial and angular information from real-world scenes, and it is beneficial to fully integrate these two pieces of information to improve image super-resolution (SR). However, most of the existing approaches to LF image SR cannot fully fuse the information at the spatial and angular lev...

Light field (LF) depth estimation is a crucial basis for LF-related applications. Most existing methods are based on the Lambertian assumption and cannot deal with non-Lambertian surfaces represented by transparent objects and mirrors. In this paper, we propose a novel Adaptive-Cross-Operator-based(ACO) depth estimation algorithm for non-Lambertian...

The problem of vehicle re-identification in surveillance scenarios has grown in popularity as a research topic. Deep learning has been successfully applied in re-identification tasks in the last few years due to its superior performance. However, deep learning approaches require a large volume of training data, and it is particularly crucial in veh...

The vehicle re-identification (V-ReID) task is critical in urban surveillance and can be used for a variety of purposes. We propose a novel augmentation method to improve the V-ReID performance. Our deep learning framework mainly consists of a local rotation transformation and a target selection module. In particular, we begin by using a random sel...

Image caption is textual explanation automatically generated by a computer according to the content in an image. It involves both image and natural language processing, and thus becomes an important research topic in pattern recognition. Deep learning has been successful in accomplishing this task, and the quality of captions generated by existing...

With the development of industrial processes, how to effectively diagnose the faults in increasingly complex production process has attracted widespread attention. It is worth noting that there may be multiple types of faults in the actual industrial process, and there is extreme class imbalance between the normal samples and the fault samples. The...

Fault diagnosis, as an important approach to ensure the safety and stability of industrial processes, has been widely studied in recent years. During the running process, it is noted that the normal data is always much more than the fault data, which demonstrates imbalanced characteristics and leads to a negative effect on the overall accuracy of f...

With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. As a widely used reliable, non-invasive technology, the Electrocardiography (ECG) data has been increasingly used for diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologist...

Vehicle re-identification (ReID) tasks are an important part of smart cities and are widely used in public security. It is extremely challenging because vehicles with different identities are generated from a uniform pipeline and cannot be distinguished based only on the subtle differences in their characteristics. To enhance the network’s ability...

Light field (LF) cameras can record multi-view images from a single scene, and these images can provide spatial and angular information to improve the performance of image super-resolution (SR). However, it is a challenge to incorporate distinctive information from different LF views. At the same time, due to the inherent resolution of the image se...

Multi-Object Tracking (MOT) methods within Tracking-by-Detection paradigm are usually modeled as graph problem. It is challenging to associate objects in dense scenes with frequent occlusion. To further model object interactions and repair detection errors, we use graph network to extract embeddings for data association. Graph neural network makes...

Automatic and accurate classification of Alzheimer’s disease is a challenging and promising task. Fully Convolutional Network (FCN) can classify images at the pixel level. Adding an attention mechanism to the Fully Convolutional Network can effectively improve the classification performance of the model. However, the self-attention mechanism ignore...

Vehicle re-identification (V-ReID) aims at discovering an image of a specific vehicle from a set of images typically captured by different cameras. Vehicles are one of the most important objects in cross-camera target recognition systems, and recognizing them is one of the most difficult tasks due to the subtle differences in the visible characteri...

This article introduces a multiple classifier method to improve the performance of concatenate-designed neural networks, such as ResNet and DenseNet, with the purpose of alleviating the pressure on the final classifier. We give the design of the classifiers, which collects the features produced between the network sets, and present the constituent...

Fault diagnosis plays an important role in ensuring process safety. It is noted that imbalance between fault data and normal data always exists, and multifault obviously outranges a single fault in common, which leads to more challenges to fault diagnosis. In this article, an imbalanced multifault diagnosis method based on bias weights AdaBoost (BW...

With live street videos posted online, the Macao Government provides means to the general public to assess the latest road traffic conditions. After reviewing over these videos, a person may decide to change the travel route from the one he or she initially plans to take. To let road users make decisions better and faster, it would be desirable to...

For intelligent transportation system, Multiple Object Tracking (MOT) is more challenging from traditional static surveillance camera to mobile devices of Internet of Things (IOT). To cope with this problem, previous works always rely on additional information from multi-vision, various sensors or pre-calibration. Only based on a monocular camera,...

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and...

In software engineering, code review controls code quality and prevents bugs. Although many commits to a codebase add features, some commits are code refactoring, including renaming of identifiers. Reviewing code refactoring requires a bit of different efforts than that of reviewing functional changes. For instance, renaming an identifier has to ma...

Pattern matching has been widely adopted in functional programming languages, and is gradually getting popular in OO languages, from Scala to Python. The structural pattern matching currently in use has its foundation on algebraic data types from functional languages. To better reflect the pointer structures of OO programs, we propose a pattern mat...

Deep learning has been successfully applied on Chest X-ray (CXR) images for disease classification. To support remote medical services (e.g., online diagnosis services), such systems can be deployed on smartphones by patients or doctors to take CXR photographs using the cameras on smartphones. However, photograph introduces visual artifacts such as...

Discrete orthogonal matrices have applications in information coding and cryptography. It is often challenging to generate discrete orthogonal matrices. A common approach widely in use is to discretize continuous orthogonal functions that have been discovered. The need of such continuous functions is restrictive. Polynomials, as the simplest class...

Object detectors often suffer from multiple performance limitations which may be attenuated with larger training datasets, improved training techniques, and complex detection models. However, such strategies are complex and time-consuming for applications requiring fast deployments. We propose a Simple Fusion of Object Detectors (SFOD) late ensembl...

This article introduces a novel RNN unit inspired by GRU, namely the Content-Adaptive Recurrent Unit (CARU). The design of CARU contains all the features of GRU but requires fewer training parameters. We make use of the concept of weights in our design to analyze the transition of hidden states. At the same time, we also describe how the content ad...

This article introduces a recurrent CNN based framework for the classification of arbitrary length text in natural sentence. In our model, we present a complete CNN design with recurrent structure to capture the contextual information as far as possible when learning sentences, which allows arbitrary-length sentences and more flexibility to analyze...

Camera variance has always been a troublesome matter in person re-identification (re-ID). Recently, more and more interests have grown in alleviating the camera variance problem by data augmentation through generative models. However, these methods, mostly based on image-level generative adversarial networks (GANs), require huge computational power...

Multi-object tracking can be characterized as a data association problem. The advantage of RNN in processing temporal dependence makes it an ideal selection in data association. When factors such as scene congestion and weak illumination cause detection failure especially long intervals, association is often very difficult and eventually leads to t...

In recent years, the demand for intelligent devices related to the Internet of Things (IoT) is rapidly increasing. In the field of computer vision, many algorithms have been pre-installed in IoT devices to achieve higher efficiency, such as face recognition, area detection, target tracking, etc. Tracking is an important but complex task that needs...

Recently, most multiple object tracking (MOT) algorithms adopt the idea of tracking-by-detection. Relevant research shows that the performance of the detector obviously affects the tracker, while the improvement of detector is gradually slowing down in recent years. Therefore, trackers using tracklet (short trajectory) are proposed to generate more...

Data association is one of the key research in tracking-bydetection framework. Due to frequent interactions among targets, there are various relationships among trajectories in crowded scenes which leads to problems in data association, such as association ambiguity, association omission, etc. To handle these problems, we propose hypothesis-testing...

Person re-identification (ReID) is an important application of Internet of Things (IoT). ReID recognizes pedestrians across camera views at different locations and time, which is usually treated as a ranking task. An essential part of this task is the hard sample mining. Technically, two strategies could be employed, i.e., global hard mining and lo...

Pedestrian detection has a wide range of real-world critical applications including security and management of emergency scenarios. In critical applications, detection recall and precision are both essential to ensure the correct detection of all pedestrians. The development and deployment of object detection vision-based models is a time-consuming...

Unfortunately the authors of this contribution missed to add an acknowledgment. The acknowledgment should read as follows: Acknowledgement: This study is partially supported by the National Key R&D Program of China (No.2017YFC0806500), the National Natural Science Foundation of China (No.61861166002), the Science and Technology Development Fund of...

Prototyping is an effective and efficient way of requirements validation to avoid introducing errors in the early stage of software development. However, manually developing a prototype of a software system requires additional efforts, which would increase the overall cost of software development. In this article, we present an approach with a deve...

Prototyping is an effective and efficient way of requirements validation to avoid introducing errors in the early stage of software development. Our previous work presents a tool RM2PT to automatically generate prototypes from requirements models. The stakeholders can easily check whether the requirements reflect their real needs by investigating t...

Due to the frequent interaction between targets in real-world scenarios, various data association problems, such as association ambiguities and association failure, are caused by potential correlation between interactive tracklets, especially during crowded and cluttered scenes. To overcome the non-intuitionistic of tracklet interaction, spatio-tem...

People detection and counting systems are highly valuable in multiple situations including managing emergency situations and efficiently allocating resources. However, most people counting systems are based on fixed sensors or fixed cameras, which lack flexibility and convenience. In this paper, we have developed a vision-based mobile people counti...

Prototyping is an effective and efficient way of requirement validation to avoid introducing errors in the early stage of software development. However, manually developing a prototype of a software system requires additional efforts, which would increase the overall cost of software development. Based on our proposed approach, we develop RM2PT: a...

Person re-identification is an important technology for target association in surveillance applications. Recently, sparse representation-based classification has been applied to person re-identification with the advantage of discriminative feature extraction and has produced excellent results. The dictionary learning (DL) method is vital to the spa...

Model checking as a computer-assisted verification method is widely used in many fields to verify whether a design model satisfies the requirements specifications of the target system. In practice, it is difficult to design a system without the sophisticated requirements analysis. Unlike other model checking tools, Labelled Transition System Analys...