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学者姓名：尹宝才
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Abstract ：
With the continuous emergence of new knowledge, Knowledge Graph (KG) typically suffers from the incompleteness problem, hindering the performance of downstream applications. Thus, Knowledge Graph Completion (KGC) has attracted considerable attention. However, existing KGC methods usually capture the coarse-grained information by directly interacting with the entity and relation, ignoring the important fine-grained information in them. To capture the fine-grained information, in this paper, we divide each entity/relation into several segments and propose a novel Multi-Level Interaction (MLI) based KGC method, which simultaneously interacts with the entity and relation at the fine-grained level and the coarse-grained level. The fine-grained interaction module applies the Gate Recurrent Unit (GRU) mechanism to guarantee the sequentiality between segments, which facilitates the fine-grained feature interaction and does not obviously sacrifice the model complexity. Moreover, the coarse-grained interaction module designs a High-order Factorized Bilinear (HFB) operation to facilitate the coarse-grained interaction between the entity and relation by applying the tensor factorization based multi-head mechanism, which still effectively reduces its parameter scale. Experimental results show that the proposed method achieves state-of-the-art performances on the link prediction task over five well-established knowledge graph completion benchmarks.
Keyword ：
representation learning representation learning knowledge graph embedding knowledge graph embedding attention network attention network Knowledge graph completion Knowledge graph completion
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| GB/T 7714 | Wang, Jiapu , Wang, Boyue , Gao, Junbin et al. Multi-Level Interaction Based Knowledge Graph Completion [J]. | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING , 2024 , 32 : 386-396 . |
| MLA | Wang, Jiapu et al. "Multi-Level Interaction Based Knowledge Graph Completion" . | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 32 (2024) : 386-396 . |
| APA | Wang, Jiapu , Wang, Boyue , Gao, Junbin , Hu, Simin , Hu, Yongli , Yin, Baocai . Multi-Level Interaction Based Knowledge Graph Completion . | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING , 2024 , 32 , 386-396 . |
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Abstract ：
The accuracy of graph representation learning is highly dependent on the precise characterization of node relationships. However, representing the complex and diverse networks in the real world using a single type of node or link is challenging, often resulting in incomplete information. Moreover, different types of nodes and links convey rich information, which makes it difficult to design a graph network that can integrate diverse links. This paper introduces a novel multi-view and multi-layer attention model designed to optimize node embeddings for semi-supervised node classification. The proposed model exploits various types of inter -node links and employs the Hilbert-Schmidt independence criterion to maximize the dissimilarity between distinct node relationships. Furthermore, the multi-layer attention mechanism is used to discern the impact of different neighboring nodes and relationships between various node relationships. The performance of the proposed model, MVMA-GCN, was assessed on numerous real-world multi-view datasets. It was observed that MVMA-GCN consistently outperformed existing models, demonstrating superior accuracy in semi-supervised classification tasks. We have made our code publicly available at here to ensure the reproducibility of our results.
Keyword ：
Graph analysis Graph analysis Semi-supervised classification Semi-supervised classification Graph convolutional networks Graph convolutional networks Graph neural networks Graph neural networks
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| GB/T 7714 | Zhang, Pengyu , Zhang, Yong , Wang, Jingcheng et al. MVMA-GCN: Multi-view multi-layer attention graph convolutional networks [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
| MLA | Zhang, Pengyu et al. "MVMA-GCN: Multi-view multi-layer attention graph convolutional networks" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 126 (2023) . |
| APA | Zhang, Pengyu , Zhang, Yong , Wang, Jingcheng , Yin, Baocai . MVMA-GCN: Multi-view multi-layer attention graph convolutional networks . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
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Abstract ：
To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good"graph structure and achieving the message passing for signals supported on the learned graph. However, the data corrupted by noise may make the learned graph structure unreliable. In this paper, we propose an adaptive graph convolutional clustering network that alternatively adjusts the graph structure and node representation layer-by-layer with back-propagation. Specifically, we design a Graph Structure Learning layer before each Graph Convolutional layer to learn the sparse graph structure from the node representations, where the graph structure is implicitly determined by the solution to the optimal self-expression problem. This is one of the first works that uses an optimization process as a Graph Network layer, which is obviously different from the function operation in traditional deep learning layers. An efficient iterative optimization algorithm is given to solve the optimal self-expression problem in the Graph Structure Learning layer. Experimental results show that the proposed method can effectively defend the negative effects of inaccurate graph structures. The code is available at https://github.com/HeXiax/SSGNN. & COPY; 2023 Elsevier Ltd. All rights reserved.
Keyword ：
Graph convolutional network Graph convolutional network Subspace clustering Subspace clustering Graph structure learning Graph structure learning
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| GB/T 7714 | He, Xiaxia , Wang, Boyue , Li, Ruikun et al. Graph structure learning layer and its graph convolution clustering application [J]. | NEURAL NETWORKS , 2023 , 165 : 1010-1020 . |
| MLA | He, Xiaxia et al. "Graph structure learning layer and its graph convolution clustering application" . | NEURAL NETWORKS 165 (2023) : 1010-1020 . |
| APA | He, Xiaxia , Wang, Boyue , Li, Ruikun , Gao, Junbin , Hu, Yongli , Huo, Guangyu et al. Graph structure learning layer and its graph convolution clustering application . | NEURAL NETWORKS , 2023 , 165 , 1010-1020 . |
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Abstract ：
Reconstructing 3D human mesh from monocular images has been extensively studied. However, the existing non-parametric reconstruction methods are inefficient when modeling vertex relationship concerning human information due to they generally adopt an uniform template mesh. To this end, this paper proposes a novel hypergraph-based human mesh hierarchical representation that enables the expression of vertices at body, parts, and vertices perspectives, corresponding to global, local and individual, respectively. And then a novel HyperGraph Attention-based human mesh reconstruction network (HGaMRNet) is put forward in turn, which mainly consists of two modules and can efficiently capture human information from different granularities of the mesh. Specifically, the first module, Body2Parts, decouples a body into local parts, and leverages Mix-Attention (MAT) based feature encoder to learn visual cues and semantic information of the parts for capturing complex human kinematic relationships from monocular images. The second module, Part2Vertices, transfers part features to the corresponding vertices through an adaptive incidence matrix, and utilizes a HyperGraph Attention network to update the vertex features. This is conductive to learning the finegrained morphological information of a human mesh. All in one, supported by the hypergraph-based hierarchical representation of the human mesh, HGaMRNet balances the effects of neighbor vertex from different levels properly and eventually promotes the reconstruction accuracy of human mesh. Experiments conducted on both Human3.6M and 3DPW datasets show that HGaMRNet outperforms most of the existing image-based human mesh reconstruction methods.& COPY; 2023 Elsevier Ltd. All rights reserved.
Keyword ：
Human mesh reconstruction Human mesh reconstruction Hierarchical representation Hierarchical representation Mix-attention Mix-attention HyperGraph HyperGraph HyperGraph attention HyperGraph attention
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| GB/T 7714 | Hao, Chenhui , Kong, Dehui , Li, Jinghua et al. HyperGraph based human mesh hierarchical representation and reconstruction from a single image [J]. | COMPUTERS & GRAPHICS-UK , 2023 , 115 : 339-347 . |
| MLA | Hao, Chenhui et al. "HyperGraph based human mesh hierarchical representation and reconstruction from a single image" . | COMPUTERS & GRAPHICS-UK 115 (2023) : 339-347 . |
| APA | Hao, Chenhui , Kong, Dehui , Li, Jinghua , Liu, Caixia , Yin, Baocai . HyperGraph based human mesh hierarchical representation and reconstruction from a single image . | COMPUTERS & GRAPHICS-UK , 2023 , 115 , 339-347 . |
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Abstract ：
LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent proposal-free methods accelerate the algorithm, but their effectiveness and efficiency are still limited owing to the difficulty of modeling non-existent instance centers and the costly center-based clustering modules. To achieve accurate and real-time LiDAR panoptic segmentation, a novel center focusing network (CFNet) is introduced. Specifically, the center focusing feature encoding (CFFE) is proposed to explicitly understand the relationships between the original LiDAR points and virtual instance centers by shifting the LiDAR points and filling in the center points. Moreover, to leverage the redundantly detected centers, a fast center deduplication module (CDM) is proposed to select only one center for each instance. Experiments on the SemanticKITTI and nuScenes panoptic segmentation benchmarks demonstrate that our CFNet outperforms all existing methods by a large margin and is 1.6 times faster than the most efficient method.
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| GB/T 7714 | Li, Xiaoyan , Zhang, Gang , Wang, Boyue et al. Center Focusing Network for Real-Time LiDAR Panoptic Segmentation [J]. | CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) , 2023 : 13425-13434 . |
| MLA | Li, Xiaoyan et al. "Center Focusing Network for Real-Time LiDAR Panoptic Segmentation" . | CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2023) : 13425-13434 . |
| APA | Li, Xiaoyan , Zhang, Gang , Wang, Boyue , Hu, Yongli , Yin, Baocai . Center Focusing Network for Real-Time LiDAR Panoptic Segmentation . | CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) , 2023 , 13425-13434 . |
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Abstract ：
Distracted driving has become one of the main contributors to traffic accidents. It is therefore of great interest for intelligent vehicles to establish a distraction detection system that can continuously monitor driver behavior and respond accordingly. Although significant progress has been made in the existing research, most of them focus on extracting either local features or global features while ignoring the other one. To make full use of both local features and global features, we integrate multi-source perception information and propose a novel dual-channel feature extraction model based on CNN and Transformer. In order to improve the model's fitting ability to time series data, the CNN channel and Transformer channel are modeled separately using the mid-point residual structure. The scaling factors in the residual structure are regarded as hyperparameters, and a penalized validation method based on bilevel optimization is introduced to obtain the optimal values automatically. Extensive experiments and comparison with the state-of-the-art methods on a multimodal dataset of driver distraction validate the effectiveness of the proposed method.
Keyword ：
Convolutional neural network Convolutional neural network Driver distraction detection Driver distraction detection Transformer Transformer Multimodal Multimodal Hyperparameter optimization Hyperparameter optimization
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| GB/T 7714 | Mou, Luntian , Chang, Jiali , Zhou, Chao et al. Multimodal driver distraction detection using dual-channel network of CNN and Transformer [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 234 . |
| MLA | Mou, Luntian et al. "Multimodal driver distraction detection using dual-channel network of CNN and Transformer" . | EXPERT SYSTEMS WITH APPLICATIONS 234 (2023) . |
| APA | Mou, Luntian , Chang, Jiali , Zhou, Chao , Zhao, Yiyuan , Ma, Nan , Yin, Baocai et al. Multimodal driver distraction detection using dual-channel network of CNN and Transformer . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 234 . |
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Abstract ：
Knowledge Graph Completion (KGC) aims at inferring missing entities or relations by embedding them in a low-dimensional space. However, most existing KGC methods generally fail to handle the complex concepts hidden in triplets, so the learned embeddings of entities or relations may deviate from the true situation. In this article, we propose a novel Multi-concept Representation Learning (McRL) method for the KGC task, which mainly consists of a multi-concept representation module, a deep residual attention module, and an interaction embedding module. Specifically, instead of the single-feature representation, the multi-concept representation module projects each entity or relation to multiple vectors to capture the complex conceptual information hidden in them. The deep residual attention module simultaneously explores the inter- and intra-connection between entities and relations to enhance the entity and relation embeddings corresponding to the current contextual situation. Moreover, the interaction embedding module further weakens the noise and ambiguity to obtain the optimal and robust embeddings. We conduct the link prediction experiment to evaluate the proposed method on several standard datasets, and experimental results show that the proposed method outperforms existing state-of-the-art KGC methods.
Keyword ：
attention network attention network Knowledge graph completion Knowledge graph completion multi-concept representation multi-concept representation
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| GB/T 7714 | Wang, Jiapu , Wang, Boyue , Gao, Junbin et al. Multi-Concept Representation Learning for Knowledge Graph Completion [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2023 , 17 (1) . |
| MLA | Wang, Jiapu et al. "Multi-Concept Representation Learning for Knowledge Graph Completion" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 17 . 1 (2023) . |
| APA | Wang, Jiapu , Wang, Boyue , Gao, Junbin , Hu, Yongli , Yin, Baocai . Multi-Concept Representation Learning for Knowledge Graph Completion . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2023 , 17 (1) . |
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Abstract ：
In the paper, we propose a novel Data-aware relation graph convolutional neural network (DAR-GCN) for AU recognition. With learning and updating the relation dynamically, it facilitates modeling the potential dynamic individual facial expressing manner and accordingly improves the AU recognition under the unconstrained environment. Taking the psychological research knowledge of AUs as a reference, we adopt the consensus widely-used AUs and six basic emotions as vertexes, and their co-occurrence or ex-occurrence relations between AUs and the emotion dependent relation as the edges to construct the graph. Moreover, the Data-aware relation Graph Generator (DAR-GG) module is proposed to learn the relations with data-driven metric learning. This proposed scheme is benefit for calculating and updating AU relations from data, which facilitates to extract specific relations causing by individual expressing characteristics as well as inherent relations due to facial anatomical structure. Comparative experiments are done on three public datasets: CK+, RAF-AU and DISFA. Experimental results demonstrate that our proposed method achieves a higher AU recognition accuracy rate than the baseline based on the graph with fixed AU relations defined from the psychological knowledge. Additionally, our proposed approach outperforms several existing state-of-the-art AU recognition method by utilizing GCN-based dynamic AU relations learning strategies. (C) 2022 Elsevier B.V. All rights reserved.
Keyword ：
AU recognition AU recognition Metric learning Metric learning GCN GCN FACS FACS Relation representation Relation representation
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| GB/T 7714 | Jia, Xibin , Zhou, Yuhan , Li, Weiting et al. Data-aware relation learning-based graph convolution neural network for facial action unit recognition [J]. | PATTERN RECOGNITION LETTERS , 2022 , 155 : 100-106 . |
| MLA | Jia, Xibin et al. "Data-aware relation learning-based graph convolution neural network for facial action unit recognition" . | PATTERN RECOGNITION LETTERS 155 (2022) : 100-106 . |
| APA | Jia, Xibin , Zhou, Yuhan , Li, Weiting , Li, Jinghua , Yin, Baocai . Data-aware relation learning-based graph convolution neural network for facial action unit recognition . | PATTERN RECOGNITION LETTERS , 2022 , 155 , 100-106 . |
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Abstract ：
Recovering the geometry of an object from a single depth image is an interesting yet challenging problem. While previous learning based approaches have demonstrated promising performance, they don't fully explore spatial relationships of objects, which leads to unfaithful and incomplete 3D reconstruction. To address these issues, we propose a Spatial Relationship Preserving Adversarial Network (SRPAN) consisting of 3D Capsule Attention Generative Adversarial Network (3DCAGAN) and 2D Generative Adversarial Network (2DGAN) for coarse-to-fine 3D reconstruction from a single depth view of an object. Firstly, 3DCAGAN predicts the coarse geometry using an encoder-decoder based generator and a discriminator. The generator encodes the input as latent capsules represented as stacked activity vectors with local-to-global relationships (i.e., the contribution of components to the whole shape), and then decodes the capsules by modeling local-to-local relationships (i.e., the relationships among components) in an attention mechanism. Afterwards, 2DGAN refines the local geometry slice-by-slice, by using a generator learning a global structure prior as guidance, and stacked discriminators enforcing local geometric constraints. Experimental results show that SRPAN not only outperforms several state-of-the-art methods by a large margin on both synthetic datasets and real-world datasets, but also reconstructs unseen object categories with a higher accuracy.
Keyword ：
latent capsule latent capsule 3D reconstruction 3D reconstruction a single depth view a single depth view self-attention self-attention
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| GB/T 7714 | Liu, Caixia , Kong, Dehui , Wang, Shaofan et al. A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View [J]. | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2022 , 18 (4) . |
| MLA | Liu, Caixia et al. "A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View" . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 18 . 4 (2022) . |
| APA | Liu, Caixia , Kong, Dehui , Wang, Shaofan , Li, Jinghua , Yin, Baocai . A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2022 , 18 (4) . |
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Abstract ：
Deep clustering has achieved great success as its powerful ability to learn effective repre-sentation. Especially, graph network clustering has attracted more and more attention. Considering the great success of Graph Autoencoder (GAE) in encoding the graph structure and Deep Autoencoder (DAE) in extracting valuable representations from the data itself, in this paper, we construct an Adversarially regularized Joint Structured Clustering Network (AJSCN) by integrating GAE and DAE. The framework links GAE and DAE together by trans-ferring the representation learned by DAE to the corresponding layer of GAE to alleviate the over-smoothing problem. Furthermore, the latent representation learned by GAE is enforced to match a prior distribution via an adversarial training scheme to avoid the free of any structure of latent space. We design a joint supervision mechanism to improve the clustering performance consisting of self-supervision and mutual supervision. The self -supervision is to learn more compact representations, and mutual-supervision makes dif-ferent representations more consistent. Experiment results demonstrate the superiority of the proposed model against the state-of-the-art algorithms and achieve significant improvement on six benchmark datasets. (c) 2022 Elsevier Inc. All rights reserved.
Keyword ：
Deep clustering Deep clustering Graph Autoencoder Graph Autoencoder Deep Autoencoder Deep Autoencoder Adversarial training Adversarial training Self-supervision Self-supervision
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| GB/T 7714 | Yang, Yachao , Ju, Fujiao , Sun, Yanfeng et al. Adversarially regularized joint structured clustering network [J]. | INFORMATION SCIENCES , 2022 , 615 : 136-151 . |
| MLA | Yang, Yachao et al. "Adversarially regularized joint structured clustering network" . | INFORMATION SCIENCES 615 (2022) : 136-151 . |
| APA | Yang, Yachao , Ju, Fujiao , Sun, Yanfeng , Gao, Junbin , Yin, Baocai . Adversarially regularized joint structured clustering network . | INFORMATION SCIENCES , 2022 , 615 , 136-151 . |
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