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学者姓名：马楠
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Abstract ：
Cross-modal 3D shape retrieval is a crucial and widely applied task in the field of 3D vision. Its goal is to construct retrieval representations capable of measuring the similarity between instances of different 3D modalities. However, existing methods face challenges due to the performance bottlenecks of single-modal representation extractors and the modality gap across 3D modalities. To tackle these issues, we propose a Heterogeneous Dynamic Graph Representation (HDGR) network, which incorporates context-dependent dynamic relations within a heterogeneous framework. By capturing correlations among diverse 3D objects, HDGR overcomes the limitations of ambiguous representations obtained solely from instances. Within the context of varying mini-batches, dynamic graphs are constructed to capture proximal intra-modal relations, and dynamic bipartite graphs represent implicit cross-modal relations, effectively addressing the two challenges above. Subsequently, message passing and aggregation are performed using Dynamic Graph Convolution (DGConv) and Dynamic Bipartite Graph Convolution (DBConv), enhancing features through heterogeneous dynamic relation learning. Finally, intra-modal, cross-modal, and self-transformed features are redistributed and integrated into a heterogeneous dynamic representation for cross-modal 3D shape retrieval. HDGR establishes a stable, context-enhanced, structure-aware 3D shape representation by capturing heterogeneous inter-object relationships and adapting to varying contextual dynamics. Extensive experiments conducted on the ModelNet10, ModelNet40, and real-world ABO datasets demonstrate the state-of-the-art performance of HDGR in cross-modal and intra-modal retrieval tasks. Moreover, under the supervision of robust loss functions, HDGR achieves remarkable cross-modal retrieval against label noise on the 3D MNIST dataset. The comprehensive experimental results highlight the effectiveness and efficiency of HDGR on cross-modal 3D shape retrieval.
Keyword ：
Representation learning Representation learning dynamic graph dynamic graph Noise measurement Noise measurement 3D vision 3D vision Shape Shape Three-dimensional printing Three-dimensional printing Solid modeling Solid modeling Convolution Convolution Correlation Correlation Cross modal retrieval Cross modal retrieval heterogeneous graph heterogeneous graph Point cloud compression Point cloud compression representation learning representation learning Cross-modal retrieval Cross-modal retrieval Bipartite graph Bipartite graph
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| GB/T 7714 | Dai, Yue , Feng, Yifan , Ma, Nan et al. Cross-Modal 3D Shape Retrieval via Heterogeneous Dynamic Graph Representation [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2025 , 47 (4) : 2370-2387 . |
| MLA | Dai, Yue et al. "Cross-Modal 3D Shape Retrieval via Heterogeneous Dynamic Graph Representation" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 47 . 4 (2025) : 2370-2387 . |
| APA | Dai, Yue , Feng, Yifan , Ma, Nan , Zhao, Xibin , Gao, Yue . Cross-Modal 3D Shape Retrieval via Heterogeneous Dynamic Graph Representation . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2025 , 47 (4) , 2370-2387 . |
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Abstract ：
A three-dimensional MOOC analysis framework was developed, focusing on platform design, organizational mechanisms, and course construction. This framework aims to investigate the current situation of big data MOOCs in the intelligent era, particularly from the perspective of improving the mental health of college students; moreover, the framework summarizes the construction experience and areas for improvement. The construction of 525 big data courses on 16 MOOC platforms is compared and analyzed from three aspects: the platform (including platform construction, resource quantity, and resource quality), organizational mechanism (including the course opening unit, teacher team, and learning norms), and course construction (including course objectives, teaching design, course content, teaching organization, implementation, teaching management, and evaluation). Drawing from the successful practices of international big data MOOCs and excellent Chinese big data MOOCs, and considering the requirements of authoritative government documents, such as the no. 8 document (J.G. [2019]), no. 3 document (J.G. [2015]), no. 1 document (J.G. [2022]), as well as the "Educational Information Technology Standard CELTS-22-Online Course Evaluation Standard", recommendations about the platform, organizational mechanism, and course construction are provided for the future development of big data MOOCs in China.
Keyword ：
investigation investigation intelligent era intelligent era big data big data analysis analysis MOOC construction MOOC construction
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| GB/T 7714 | Sang, Hongfeng , Ma, Liyi , Ma, Nan . Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students [J]. | INFORMATION , 2023 , 14 (9) . |
| MLA | Sang, Hongfeng et al. "Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students" . | INFORMATION 14 . 9 (2023) . |
| APA | Sang, Hongfeng , Ma, Liyi , Ma, Nan . Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students . | INFORMATION , 2023 , 14 (9) . |
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Abstract ：
Person re- identification (Re-ID) has become a hot research topic due to its widespread applications. Conducting person Re-ID in video sequences is a practical requirement, in which the crucial challenge is how to pursue a robust video representation based on spatial and temporal features. However, most of the previous methods only consider how to integrate part-level features in the spatio-temporal range, while how to model and generate the part-correlations is little exploited. In this paper, we propose a skeleton-based dynamic hypergraph framework, namely Skeletal Temporal Dynamic Hypergraph Neural Network (ST-DHGNN) for person Re-ID, which resorts to modeling the high-order correlations among various body parts based on a time series of skeletal information. Specifically, multi-shape and multi-scale patches are heuristically cropped from feature maps, constituting spatial representations in different frames. A joint-centered hypergraph and a bone-centered hypergraph are constructed in parallel from multiple body parts (i.e., head, trunk, and legs) with spatio-temporal multi-granularity in the entire video sequence, in which the graph vertices representing regional features and hyperedges denoting relationships. Dynamic hypergraph propagation containing the re- planning module and the hyperedge elimination module is proposed to better integrate features among vertices. Feature aggregation and attention mechanisms are also adopted to obtain a better video representation for person Re-ID. Experiments show that the proposed method performs significantly better than the state-of-the-art on three video-based person Re-ID datasets, including iLIDS-VID, PRID-2011, and MARS.
Keyword ：
hypergraph hypergraph Task analysis Task analysis Person re-identification Person re-identification Video sequences Video sequences Feature extraction Feature extraction Legged locomotion Legged locomotion Correlation Correlation spatio-temporal correlation spatio-temporal correlation Head Head Joints Joints hypergraph learning hypergraph learning
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| GB/T 7714 | Lu, Jiaxuan , Wan, Hai , Li, Peiyan et al. Exploring High-Order Spatio-Temporal Correlations From Skeleton for Person Re-Identification [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2023 , 32 : 949-963 . |
| MLA | Lu, Jiaxuan et al. "Exploring High-Order Spatio-Temporal Correlations From Skeleton for Person Re-Identification" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 32 (2023) : 949-963 . |
| APA | Lu, Jiaxuan , Wan, Hai , Li, Peiyan , Zhao, Xibin , Ma, Nan , Gao, Yue . Exploring High-Order Spatio-Temporal Correlations From Skeleton for Person Re-Identification . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2023 , 32 , 949-963 . |
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Abstract ：
With the development of urbanization, the number of vehicles is gradually increasing, and vehicles are gradually developing in the direction of intelligence. How to ensure that the data of intelligent vehicles is not tampered in the process of transmission to the cloud is the key problem of current research. Therefore, we have established a data security transmission system based on blockchain. First, we collect and filter vehicle data locally, and then use blockchain technology to transmit key data. Through the smart contract, the key data is automatically and accurately transmitted to the surrounding node vehicles, and the vehicles transmit data to each other to form a transaction and spread to the whole network. The node data is verified through the node data consensus protocol of intelligent vehicle data security transmission system, and written into the block to form a blockchain. Finally, the vehicle user can query the transaction record through the vehicle address. The results show that we can safely and accurately transmit and query vehicle data in the blockchain database.
Keyword ：
Internet of Vehicles Internet of Vehicles Data security Data security smart city smart city Blockchain Blockchain
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| GB/T 7714 | Chen, Kai , Wu, Hongjun , Xu, Cheng et al. An Intelligent Vehicle Data Security System based on Blockchain for Smart City [J]. | 2022 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY, HUMAN-COMPUTER INTERACTION AND ARTIFICIAL INTELLIGENCE, VRHCIAI , 2022 : 227-231 . |
| MLA | Chen, Kai et al. "An Intelligent Vehicle Data Security System based on Blockchain for Smart City" . | 2022 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY, HUMAN-COMPUTER INTERACTION AND ARTIFICIAL INTELLIGENCE, VRHCIAI (2022) : 227-231 . |
| APA | Chen, Kai , Wu, Hongjun , Xu, Cheng , Ma, Nan , Dai, Songyin , Liu, Hongzhe . An Intelligent Vehicle Data Security System based on Blockchain for Smart City . | 2022 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY, HUMAN-COMPUTER INTERACTION AND ARTIFICIAL INTELLIGENCE, VRHCIAI , 2022 , 227-231 . |
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Abstract ：
With the development of deep learning, graph neural networks have attracted ever-increasing attention due to their exciting results on handling data from non-Euclidean space in recent years. However, existing graph neural networks frameworks are designed based on simple graphs, which limits their ability to handle data with complex correlations. Therefore, in some special cases, especially when the data have interdependence, the complexity of the data poses a significant challenge to traditional graph neural networks algorithm. To overcome this challenge, researchers model the complex relationship of data by constructing hypergraph, and use hypergraph neural networks to learn the complex relationship within data, so as to effectively obtain higher-order feature representations of data. In this paper, we first review the basics of hypergraph, then provide a detailed analysis and comparison of some recently proposed hypergraph neural networks algorithm, next some applications of hypergraph neural networks for action recognition are listed, and finally propose potential future research directions of hypergraph neural networks to provide ideas for subsequent research.
Keyword ：
Deep learning Deep learning Hypergraph Hypergraph Action recognition Action recognition Hypergraph neural network Hypergraph neural network
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| GB/T 7714 | Wang, Cheng , Ma, Nan , Wu, Zhixuan et al. Survey of Hypergraph Neural Networks and Its Application to Action Recognition [J]. | ARTIFICIAL INTELLIGENCE, CICAI 2022, PT II , 2022 , 13605 : 387-398 . |
| MLA | Wang, Cheng et al. "Survey of Hypergraph Neural Networks and Its Application to Action Recognition" . | ARTIFICIAL INTELLIGENCE, CICAI 2022, PT II 13605 (2022) : 387-398 . |
| APA | Wang, Cheng , Ma, Nan , Wu, Zhixuan , Zhang, Jin , Yao, Yongqiang . Survey of Hypergraph Neural Networks and Its Application to Action Recognition . | ARTIFICIAL INTELLIGENCE, CICAI 2022, PT II , 2022 , 13605 , 387-398 . |
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Abstract ：
Human action recognition has attracted extensive research efforts in recent years, in which traffic police gesture recognition is important for self-driving vehicles. One of the crucial challenges in this task is how to find a representation method based on spatial-temporal features. However, existing methods performed poorly in spatial and temporal information fusion, and how to extract features of traffic police gestures has not been well researched. This paper proposes an attention mechanism based on the improved spatial-temporal convolutional neural network (AMSTCNN) for traffic police gesture recognition. This method focuses on the action part of traffic police and uses the correlation between spatial and temporal features to recognize traffic police gestures, so as to ensure that traffic police gesture information is not lost. Specifically, AMSTCNN integrates spatial and temporal information, uses weight matching to pay more attention to the region where human action occurs, and extracts region proposals of the image. Finally, we use Softmax to classify actions after spatial-temporal feature fusion. AMSTCNN can strongly make use of the spatial-temporal information of videos and select effective features to reduce computation. Experiments on AVA and the Chinese traffic police gesture datasets show that our method is superior to several state-of-the-art methods.
Keyword ：
spatial-temporal features spatial-temporal features Intelligent interaction Intelligent interaction attention mechanism attention mechanism traffic police gestures traffic police gestures human action recognition human action recognition
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| GB/T 7714 | Wu, Zhixuan , Ma, Nan , Gao, Yue et al. Attention Mechanism Based on Improved Spatial-Temporal Convolutional Neural Networks for Traffic Police Gesture Recognition [J]. | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE , 2022 , 36 (08) . |
| MLA | Wu, Zhixuan et al. "Attention Mechanism Based on Improved Spatial-Temporal Convolutional Neural Networks for Traffic Police Gesture Recognition" . | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 36 . 08 (2022) . |
| APA | Wu, Zhixuan , Ma, Nan , Gao, Yue , Li, Jiahong , Xu, Xinkai , Yao, Yongqiang et al. Attention Mechanism Based on Improved Spatial-Temporal Convolutional Neural Networks for Traffic Police Gesture Recognition . | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE , 2022 , 36 (08) . |
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