Huihui Xu

University of Pittsburgh

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Kevin D. Ashley

University of Pittsburgh

JŠ

Jaromír Šavelka

Carnegie Mellon University

Fei Li

Universitat Politècnica de Catalunya

Mingyan Jiang

Beijing Jiaotong University

Nan Liu

SingHealth

Kevin Ashley

Intelligent Systems Research (United States)

Jing Peng

Shanghai Maritime University

Zhiquan Feng

University of Jinan

Yong Li

National Institute of Metrology

Mingyan Jiang

Shandong University

Cooperative Institutions

Intelligent Systems Research (United States)

University of Pittsburgh

Shandong University

University of Electronic Science and Technology of China

Sichuan University

First Hospital of China Medical University

Centre Tecnologic de Telecomunicacions de Catalunya

SingHealth

Duke-NUS Medical School

National University of Singapore

Author Statistics

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Research Field

Multi-Granularity Argument Mining in Legal Texts

Frontiers in artificial intelligence and applications (2022)

Huihui Xu Kevin D. Ashley

In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the tokens. Results show that token-level text classification identifies certain legal argument elements more accurately than sentence-level text classification. Token-level classification also provides greater flexibility to analyze legal texts and to gain more insight into what the model focuses on when processing a large amount of input data.

Argument (complex analysis)

Granularity

10.3233/faia220477

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Citations (8)

Argumentative Segmentation Enhancement for Legal Summarization

arXiv (Cornell University) (2023)

Huihui Xu Kevin D. Ashley

We use the combination of argumentative zoning [1] and a legal argumentative scheme to create legal argumentative segments. Based on the argumentative segmentation, we propose a novel task of classifying argumentative segments of legal case decisions. GPT-3.5 is used to generate summaries based on argumentative segments. In terms of automatic evaluation metrics, our method generates higher quality argumentative summaries while leaving out less relevant context as compared to GPT-4 and non-GPT models.

Argumentative

10.48550/arxiv.2307.05081

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Citations (2)

Multilevel Pyramid Network for Monocular Depth Estimation Based on Feature Refinement and Adaptive Fusion

Electronics (2022)

Huihui Xu Fei Li

As a traditional computer vision task, monocular depth estimation plays an essential role in novel view 3D reconstruction and augmented reality. Convolutional neural network (CNN)-based models have achieved good performance for this task. However, in the depth map recovered by some existing deep learning-based methods, local details are still lost. To generate convincing depth maps with rich local details, this study proposes an efficient multilevel pyramid network for monocular depth estimation based on feature refinement and adaptive fusion. Specifically, a multilevel spatial feature generation scheme is developed to extract rich features from the spatial branch. Then, a feature refinement module that combines and enhances these multilevel contextual and spatial information is designed to derive detailed information. In addition, we design an adaptive fusion block for improving the capability of fully connected features. The performance evaluation results on public RGBD datasets indicate that the proposed approach can recover reasonable depth outputs with better details and outperform several depth recovery algorithms from a qualitative and quantitative perspective.

Pyramid (geometry)

Feature (linguistics)

Monocular

Depth map

10.3390/electronics11162615

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Citations (4)

Depth estimation algorithm based on data-driven approach and depth cues for stereo conversion in three-dimensional displays

Optical Engineering (2016)

Huihui Xu Mingyan Jiang Fei Li

With the advances in three-dimensional (3-D) display technology, stereo conversion has attracted much attention as it can alleviate the problem of stereoscopic content shortage. In two-dimensional (2-D) to 3-D conversion, the most difficult and challenging problem is depth estimation from a single image. In order to recover a perceptually plausible depth map from a single image, a depth estimation algorithm based on a data-driven method and depth cues is presented. Based on the human visual system mechanism, which is sensitive to the foreground object, this study classifies the image into one of two classes, i.e., nonobject image and object image, and then leverages different strategies on the basis of image type. The proposed strategies efficiently extract the depth information from different images. Moreover, depth image-based rendering technology is utilized to generate stereoscopic views by combining 2-D images with their depth maps. The proposed method is also suitable for 2-D to 3-D video conversion. Qualitative and quantitative evaluation results demonstrate that the proposed depth estimation algorithm is very effective for generating stereoscopic content and producing visually pleasing and realistic 3-D views.

Depth map

Stereo image

10.1117/1.oe.55.12.123106

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Citations (2)

Carbon Emission Prediction in Thermoelectric Coupled Systems Based on Improved Ensemble Empirical Mode Decomposition and Reinforcement Learning

2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE) (2024)

Yunfei Tian Huihui Xu Jing Peng Yi Chai Yong Li

Mode (computer interface)

Ensemble Learning

10.1109/cipae64326.2024.00098

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Citations (0)

A multiscale dilated convolution and mixed-order attention-based deep neural network for monocular depth prediction

SN Applied Sciences (2022)

Huihui Xu Fei Li

Abstract Recovering precise depth information from different scenes has become a popular subject in the semantic segmentation and virtual reality fields. This study presents a multiscale dilated convolution and mixed-order attention-based deep neural network for monocular depth recovery. Specifically, we design a multilevel feature enhancement scheme to enhance and fuse high-resolution and low-resolution features on the basis of mixed-order attention. Moreover, a multiscale dilated convolution module that combines four different dilated convolutions is explored for deriving multiscale information and increasing the receptive field. Recent studies have shown that the design of loss terms is crucial to depth prediction. Therefore, an efficient loss function that combines the ℓ 1 loss, gradient loss, and classification loss is also designed to promote rich details. Experiments on three public datasets show that the presented approach achieves better performance than state-of-the-art depth prediction methods.

Convolution (computer science)

Monocular

Fuse (electrical)

Feature (linguistics)

Upsampling

Information loss

10.1007/s42452-022-05235-1

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Citations (2)

Approach of license-plate location based on video detection

Huihui Xu

The key of automatic license-plate recognition system is to locate plate quickly and exactly.An algorithm based on video de-tection to locate the plate is proposed.First pre-located the plate by detecting the region where the changes happened and dynamically updated the background using the strategy that combined the real time amendment and the long-term replacement,and then located the plate exactly with mathematical morphology.The located plate is emendated and affirmed at last.The results of experiments under dif-ferent conditions show that the proposed method is very effective and has a good prospect for application.

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Detail-preserving depth estimation from a single image based on modified fully convolutional residual network and gradient network

SN Applied Sciences (2021)

Huihui Xu Nan Liu

Abstract Predicting a convincing depth map from a monocular single image is a daunting task in the field of computer vision. In this paper, we propose a novel detail-preserving depth estimation (DPDE) algorithm based on a modified fully convolutional residual network and gradient network. Specifically, we first introduce a new deep network that combines the fully convolutional residual network (FCRN) and a U-shaped architecture to generate the global depth map. Meanwhile, an efficient feature similarity-based loss term is introduced for training this network better. Then, we devise a gradient network to generate the local details of the scene based on gradient information. Finally, an optimization-based fusion scheme is proposed to integrate the depth and depth gradients to generate a reliable depth map with better details. Three benchmark RGBD datasets are evaluated from the perspective of qualitative and quantitative, the experimental results show that the designed depth prediction algorithm is superior to several classic depth prediction approaches and can reconstruct plausible depth maps.

Benchmark (surveying)

Depth map

Monocular

Similarity (geometry)

Feature (linguistics)

10.1007/s42452-021-04882-0

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Citations (1)

Question-Answering Approach to Evaluating Legal Summaries

arXiv (Cornell University) (2023)

Huihui Xu Kevin Ashley

Traditional evaluation metrics like ROUGE compare lexical overlap between the reference and generated summaries without taking argumentative structure into account, which is important for legal summaries. In this paper, we propose a novel legal summarization evaluation framework that utilizes GPT-4 to generate a set of question-answer pairs that cover main points and information in the reference summary. GPT-4 is then used to generate answers based on the generated summary for the questions from the reference summary. Finally, GPT-4 grades the answers from the reference summary and the generated summary. We examined the correlation between GPT-4 grading with human grading. The results suggest that this question-answering approach with GPT-4 can be a useful tool for gauging the quality of the summary.

Grading (engineering)

Argumentative

10.48550/arxiv.2309.15016

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Citations (0)

Toward summarizing case decisions via extracting argument issues, reasons, and conclusions

Huihui Xu Jaromír Šavelka Kevin D. Ashley

In this paper, we assess the use of several deep learning classification algorithms as a step toward automatically preparing succinct summaries of legal decisions. Short case summaries that tease out the decision's argument structure by making explicit its issues, conclusions, and reasons (i.e., argument triples) could make it easier for the lay public and legal professionals to gain an insight into what the case is about. We have obtained a sizeable dataset of expert-crafted case summaries paired with full texts of the decisions issued by various Canadian courts. As the manual annotation of the full texts is prohibitively expensive, we explore various ways of leveraging the existing longer summaries which are much less time-consuming to annotate. We compare the performance of the systems trained on the annotations that are manually ported to the full texts from the summaries to the performance of the same systems trained on annotations that are projected from the summaries automatically. The results show the possibility of pursuing the automatic annotation in the future.

Argument (complex analysis)

Porting

10.1145/3462757.3466098

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Citations (11)