
Jiawei LiHuawei Technologies
Jiawei Li
Doctor of Philosophy
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22
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Publications (22)
Traditional QR codes consist of a grid of black-and-white square modules, which lack aesthetic appeal and meaning for human perception. This has motivated recent research to beautify the visual appearance of QR codes. However, there exists a trade-off between the visual quality and scanning-robustness of the image, causing outputs of previous works...
Beyond achieving higher compression efficiency over classical image compression codecs, deep image compression is expected to be improved with additional side information, e.g., another image from a different perspective of the same scene. To better utilize the side information under the distributed compression scenario, the existing method only im...
Recent trends in cloud computing technology effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-term support to the online application. To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-sup...
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze its consistency and privacy-preservation. Instead of deterministic greedy split rule or with simple randomness, the MRF adopts t...
Financial signal processing is a matter of great concern in FinTech. Traditionally, recurrent networks are often used to model time series, while the latest research shows that convolutional networks, especially temporal convolutional networks (TCNs), are also powerful and effective for a large number of sequence modeling tasks. The temporal con-vo...
From the mutual empowerment of two high-speed development technologies: artificial intelligence and edge computing , we propose a tailored Edge Intelligent Video Surveillance (EIVS) system. It is a scalable edge computing architecture and uses multitask deep learning for relevant computer vision tasks. Due to the potential application of different...
Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and decision-making. In this paper, we propose a Tree-Ne...
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality. In this paper, we propose a knowledge distillation based decision trees extension, dubbed rectified decision trees (ReDT), to explore the possibility of fulfilling those requirements simultaneously. Specifically, we...
The falling accident is one of the largest threats to human health, which leads to broken bones, head injury, or even death. Therefore, automatic human fall recognition is vital for the Activities of Daily Living (ADL). In this paper, we try to define multi-level computer vision tasks for the visually observed fall recognition problem and study the...
Current research has managed to train multiple Deep Neural Networks (DNNs) in affordable computing time. Then, finding a practical method to aggregate these DNNs becomes a fundamental problem. To address this, we present an unbiased combination scheme to guide the aggregation of the diverse DNNs models, by leveraging the Negative Correlation Learni...
Rain Streaks in a single image can severely damage the visual quality, and thus degrade the performance of current computer vision algorithms. To remove the rain streaks effectively, plenty of CNN-based methods have recently been developed, and obtained impressive performance. However, most existing CNN-based methods focus on network design, while...
The ensemble of Convolutional Neural Networks (CNNs) is known to be more accurate and robust than the component CNNs models. Along with the development of a fast training method, current research has managed to make an effective ensemble of several CNNs models and require no additional training cost. However, when the ensemble size of CNNs is furth...
The falling accident is one of the most threats to human health, which leads to broken bones, head injury, or even death. Therefore, automatic human fall detection is vital in the video surveillance. In this paper, we first make a multi-level computer-vision definition for human fall detection. Then, we propose a novel pose motion representation an...
How to obtain a model with good interpretability and performance has always been an important research topic. In this paper, we propose rectified decision trees (ReDT), a knowledge distillation based decision trees rectification with high interpretability, small model size, and empirical soundness. Specifically, we extend the impurity calculation a...
Noisy labels modeling makes a convolutional neural network (CNN) more robust for the image classification problem. However, current noisy labels modeling methods usually require an expectation-maximization (EM) based procedure to optimize the parameters, which is computationally expensive. In this paper, we utilize a fast annealing training method...
The goal of artistic style transfer is to transfer the style of artistic works into photos. However, the performances of existing style transfer algorithms on portraits are not very satisfactory, because the synthetic photo is either not sufficiently stylized or distorted severely in the portrait domain (i.e., foreground), which limits the use of s...