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7th International Conference on computer Information Science and Application Technology, CISAT 2024

作者： Li, Jiaxing Zhong, Zhaoqian Dalian University National and Local Joint Engineering Laboratory of Computer Aided Design Dalian China

ISBN: (纸本)9798350375107

In recent years, graph-based and sequence-based document-level relation extraction models have attracted much attention. However, when performing document-level relation extraction tasks, traditional graph-based models struggle to address the overfitting issue of graph convolutional networks when analyzing document information, and sequence-based models may not effectively capture all document features. To address the above problems, we propose a document level relation extraction model based on dendrogram with dual encoders. The model uses dual encoders to extract document features of various granularities to construct a dendrogram structure. Experimental results on the public dataset DocRED show that the performance of the proposed model is significantly improved compared to other models that solely rely on graph structure or sequence structure. © 2024 IEEE.

关键词： Encoding (symbols)

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3D Human Pose Estimation Using Two-Stream Architecture with joint Training

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computer Modeling in engineering & Sciences 2023年第10期137卷 607-629页

作者： Jian Kang Wanshu Fan Yijing Li Rui Liu Dongsheng Zhou National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian116622China Dalian Maritime University Dalian116023China

With the advancement of image sensing technology, estimating 3Dhuman pose frommonocular video has becomea hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequentaction analysis and understanding. It empowers a wide spectrum of potential applications in various areas, suchas intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methodsfor 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extractinter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationshipextractions. In this paper, we decompose the 3D joint location regression into the bone direction and length, wepropose the TCG, a temporal convolutional network incorporating Gaussian error linear units (GELU), to solvebone direction. It enablesmore inter-frame features to be captured andmakes the utmost of the feature relationshipsbetween data. Furthermore, we adopt kinematic structural information to solve bone length enhancing the use ofintra-frame joint features. Finally, we design a loss function for joint training of the bone direction estimationnetwork with the bone length estimation network. The proposed method has extensively experimented on thepublic benchmark dataset Human3.6M. Both quantitative and qualitative experimental results showed that theproposed method can achieve more accurate 3D human pose estimations.

关键词： 3D human pose improved TCN GELU kinematic structure

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ER-Net:Efficient Recalibration Network for Multi-ViewMulti-Person 3D Pose Estimation

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computer Modeling in engineering & Sciences 2023年第8期136卷 2093-2109页

作者： Mi Zhou Rui Liu Pengfei Yi Dongsheng Zhou National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian116622China School of Computer Science and Technology Dalian University of TechnologyDalian116024China

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application *** the introduction of end-to-end direct regression methods,the field has entered a new stage of ***,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal *** this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external ***,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding *** call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.

关键词： Multi-view multi-person pose estimation attention mechanism computer vision

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Dual modality prompt learning for visual question-grounded answering in robotic surgery

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 316-328页

作者： Yue Zhang Wanshu Fan Peixi Peng Xin Yang Dongsheng Zhou Xiaopeng Wei National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622LiaoningChina School of Computer Science and Technology Dalian University of TechnologyDalian 116081LiaoningChina

With recent advancements in robotic surgery,notable strides have been made in visual question answering(VQA).Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the *** limitation restricts the interpretative capacity of the VQA models and their abil-ity to explore specific image *** address this issue,this study proposes a grounded VQA model for robotic surgery,capable of localizing a specific region during answer *** inspiration from prompt learning in language models,a dual-modality prompt model was developed to enhance precise multimodal information ***,two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model.A visual complementary prompter merges visual prompt knowl-edge with visual information features to guide accurate *** textual complementary prompter aligns vis-ual information with textual prompt knowledge and textual information,guiding textual information towards a more accurate inference of the ***,a multiple iterative fusion strategy was adopted for comprehensive answer reasoning,to ensure high-quality generation of textual and grounded *** experimental results vali-date the effectiveness of the model,demonstrating its superiority over existing methods on the EndoVis-18 and End-oVis-17 datasets.

关键词： Prompt learning Visual prompt Textual prompt Grounding-answering Visual question answering

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3D Human Pose Estimation via Graph Extended Spatio-Temporal Convolutional Network 9

3D Human Pose Estimation via Graph Extended Spatio-Temporal ...

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9th International Conference on Virtual Reality, ICVR 2023

作者： Jia, Yanhui Fan, Wanshu Zhou, Dongsheng Zhang, Qiang Dalian University National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian China

ISBN: (纸本)9798350345810

3D human pose estimation is an important premise for human behavior analysis and understanding, which has a wide range of applications in intelligent transportation, human-computer interaction, and animation production. Most existing works focus on extracting the feature relationship between frames by combining spatio-temporal information to reduce the error of attitude reconstruction. However, the majority of them often suffer from insufficient joint correlation characteristics. To address this problem, we propose a Graph Expand Spatiotemporal Convolutional Network, named GESC-Net, to improve the limitation of extracting human spatial structure features. To better enrich the feature of extracting local information, we develop a learnable symmetric connection (LSC) block in the spatial structure. Moreover, a CbAttantion block is also designed to obtain a larger view of the acquisition of global structure and get more effective features. We evaluate our approach on two standard benchmark datasets: Human3.6M and HumanEva-I. The quantitative and qualitative evaluation results demonstrate that the GESC-Net can achieve better 3D human posture estimation than existing state-of-the-art methods. © 2023 IEEE.

关键词： Convolution

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PSA-CLNet: Pedestrian Search Method Based on Polarized Self-Attention and COIM Loss 9

PSA-CLNet: Pedestrian Search Method Based on Polarized Self-...

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9th International Conference on Virtual Reality, ICVR 2023

作者： Fu, Junjie Liu, Rui Yi, Pengfei Dong, Jing Zhou, Dongsheng Wei, Xiaopeng Dalian University National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350345810

Pedestrian search is a combined task of pedestrian detection and pedestrian re-identification, i.e., how to find a specific pedestrian in a panoramic picture. However, due to the impact of different imaging conditions and environments, the accuracy of pedestrian search is still low at present, which is difficult to meet the needs of practical applications. To improve the accuracy of pedestrian search, this paper proposes PSA-CLNet pedestrian search method, which integrates polarized self-attention mechanism (PSA) and cosine online instance matching (COIM) loss. Firstly, the channel attention is added to enhance the pedestrian feature information and improve the expression ability of the feature information. Secondly, spatial attention is added to enhance the areas that have more contribution to the pedestrian search task to increase the correlation between the pictures and improve the recognition ability. Meanwhile, this paper proposes a COIM loss based on the online instance matching (OIM) loss. The loss function can enlarge the class distance between pedestrians and background, and reduce the class distance between pedestrians. This could improve the accuracy of pedestrian search algorithm effectively. Finally, this paper uses ResNet as the main backbone network to verify the proposed method. The pedestrian search method proposed in this paper could achieve 96.60% of mAP index and 96.83% of top-1 index in CUHK-SYSU dataset, and achieve 48.78% of mAP index and 88.38% of top-1 index in PRW dataset. © 2023 IEEE.

关键词： COIM loss pedestrian search PSA

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DETG: Dual encoding document level relation extraction based on tree graph

DETG: Dual encoding document level relation extraction based...

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computer Information Science and Application Technology (CISAT), International Conference on

作者： Jiaxing Li Zhaoqian Zhong National and Local Joint Engineering Laboratory of Computer Aided Design Dalian University Dalian China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

关键词： Analytical models Information science Tree graphs Graph convolutional networks Semantics Knowledge graphs Feature extraction Encoding Cognition Data mining

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STTG-net:a Spatio-temporal network for human motion prediction based on transformer and graph convolution network

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 224-238页

作者： Lujing Chen Rui Liu Xin Yang Dongsheng Zhou Qiang Zhang Xiaopeng Wei tional and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622China School of Computer Science and Technology Dalian University of TechnologyDalian 116024China

In recent years,human motion prediction has become an active research topic in computer ***,owing to the complexity and stochastic nature of human motion,it remains a challenging *** previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive ***,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in ***,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each *** overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous *** experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction *** effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.

关键词： Human motion prediction Transformer Gragh convolutional network

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MCFNet: Multi-scale Cross Fusion Network for 3D Human Pose Estimation

MCFNet: Multi-scale Cross Fusion Network for 3D Human Pose E...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Dazhong Wang Rui Liu Pengfei Yi Jing Dong Dongsheng Zhou National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

3D human pose estimation has attracted increasing attention due to it frequently used in fields such as human-computer interaction. Due to the structure of the human skeleton is a kind of undirected topology, Graph Convolutional Network(GCN) is a suitable method to study 3D human pose estimation. However, traditional GCNs overlook the distinctions between joints and the implicit connections among nonadjacent joints. Additionally, the topological structure of the human body leads to significant error accumulation in pose estimation, which make the joints have higher error levels in highly flexible regions. We present Multi-scale Cross Fusion Network(MCFNet), a network that sequentially learns the human skeleton structure from joint, limb, and body semantic levels by segmenting original features. The MCFNet can combine the High-order MGCN(Modulated Graph Convolutional Network) module for joint scale capture and the Conv-SE module for limb scale feature fusion, thus achieving excellent joint feature extraction and reduction of the error accumulation. Experimental results on datasets like Human3.6M demonstrate that our proposed method surpasses the performance of state-of-the-art networks and achieves the highest accuracy.

关键词： Human computer interaction Three-dimensional displays Accuracy Network topology Graph convolutional networks Pose estimation Semantics

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MHFDN:Multi-Branch Hybrid Frequency Domain Networks for 3D Human Motion Prediction

MHFDN:Multi-Branch Hybrid Frequency Domain Networks for 3D H...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Xin Zhou Rui Liu Jing Dong Pengfei Yi Ling Wang National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Recent studies have shown great potential in 3D human motion prediction using Graph Convolutional Networks (GCN)-based modeling. However, these methods have two key problems: First, there is a lack of explicit extraction of the spatial features of joint data. Second, the relevant high-frequency components are often ignored in the feature extraction process. In order to solve these two problems, we propose a novel and universal hybrid frequency domain networks based on multi-branch structure, which contains multiple mixed frequency domain blocks to give more focus on high frequencies information during training. Through the multi-branch structure, we can learn both local and global movements of the human body. Since the bottom layer is mainly responsible for capturing high-frequency details, the top layer is more concerned with the modeling of low-frequency global information, we further introduce the structure of frequency component modification module and perform information trade-offs based on different networks layers, enabling each layer to effectively extract appropriate high-frequency and low-frequency human features. Experiments show that our method has high prediction performance and higher robustness on the Human3.6M and CMU Mocap datasets.

关键词： Training Solid modeling Three-dimensional displays Convolution Frequency-domain analysis Predictive models Feature extraction

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