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学者姓名：贾克斌
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Abstract ：
As skeleton data becomes increasingly available, Graph Convolutional Networks (GCNs) are popularly adapted to extract the spatial and temporal features for skeleton-based action recognition. However, there are still limitations to be addressed in GCN-based methods. First, the multi-level semantic features fail to be connected, making fine-grained information loss as the network deepens. Second, the cross-scale spatiotempral features fail to be simultaneously considered and refined to focus on informative areas. These limitations lead to the challenge in distinguishing the confusing actions. To address these issues, we propose a cross-scale connection (CSC) structure and a spatiotemporal refinement focus (STRF) module. The CSC aims to bridge the gap between multi-level semantic features. The STRF module refines the cross-scale spatiotemporal features to focus on informative joints in each frame. Both are embedded into the standard GCNs to form the cross-scale spatiotemporal refinement network (CSR-Net). Our proposed CSR-Net explicitly models the cross-scale spatiotemporal information among multi-level semantic representations to boost the distinguishing capability for ambiguous actions. We conduct extensive experiments to demonstrate the effectiveness of our proposed method and it outperforms state-of-the-art methods on the NTU RGB+D 60, NTU-RGB+D 120 and NW-UCLA datasets.
Keyword ：
Skeleton-based action recognition Skeleton-based action recognition cross-scale fusion cross-scale fusion graph convolutional network graph convolutional network
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| GB/T 7714 | Zhang, Yu , Sun, Zhonghua , Dai, Meng et al. Cross-Scale Spatiotemporal Refinement Learning for Skeleton-Based Action Recognition [J]. | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 : 441-445 . |
| MLA | Zhang, Yu et al. "Cross-Scale Spatiotemporal Refinement Learning for Skeleton-Based Action Recognition" . | IEEE SIGNAL PROCESSING LETTERS 31 (2024) : 441-445 . |
| APA | Zhang, Yu , Sun, Zhonghua , Dai, Meng , Feng, Jinchao , Jia, Kebin . Cross-Scale Spatiotemporal Refinement Learning for Skeleton-Based Action Recognition . | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 , 441-445 . |
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Abstract ：
Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction.
Keyword ：
brain network brain network depression prediction depression prediction EEG signal EEG signal time-frequency complexity time-frequency complexity spatial topology spatial topology graph convolutional network graph convolutional network
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| GB/T 7714 | Liu, Wei , Jia, Kebin , Wang, Zhuozheng . Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology [J]. | FRONTIERS IN NEUROSCIENCE , 2024 , 18 . |
| MLA | Liu, Wei et al. "Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology" . | FRONTIERS IN NEUROSCIENCE 18 (2024) . |
| APA | Liu, Wei , Jia, Kebin , Wang, Zhuozheng . Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology . | FRONTIERS IN NEUROSCIENCE , 2024 , 18 . |
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Abstract ：
Sleep is crucial for human health. Automatic sleep stage classification based on polysomnogram (PSG) is meaningful for the diagnosis of sleep disorders, which has attracted extensive attention in recent years. Most existing methods could not fully consider the different transitions of sleep stages and fit the visual inspection of sleep experts simultaneously. To this end, we propose a temporal multi-scale hybrid attention network, namely TMHAN, to automatically achieve sleep staging. The temporal multi-scale mechanism incorporates short-term abrupt and long-term periodic transitions of the successive PSG epochs. Furthermore, the hybrid attention mechanism includes 1-D local attention, 2-D global attention, and 2-D contextual sparse multi-head self-attention for three kinds of sequence-level representations. The concatenated representation is subsequently fed into a softmax layer to train an end-to-end model. Experimental results on two benchmark sleep datasets show that TMHAN obtains the best performance compared with several baselines, demonstrating the effectiveness of our model. In general, our work not only provides good classification performance, but also fits the actual sleep staging processes, which makes contribution for the combination of deep learning and sleep medicine.
Keyword ：
Attention mechanism Attention mechanism Temporal multi-scale mechanism Temporal multi-scale mechanism Polysomnogram Polysomnogram Sleep stage classification Sleep stage classification Biomedical signal processing Biomedical signal processing
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| GB/T 7714 | Jin, Zheng , Jia, Kebin . A temporal multi-scale hybrid attention network for sleep stage classification [J]. | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2023 , 61 (9) : 2291-2303 . |
| MLA | Jin, Zheng et al. "A temporal multi-scale hybrid attention network for sleep stage classification" . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 61 . 9 (2023) : 2291-2303 . |
| APA | Jin, Zheng , Jia, Kebin . A temporal multi-scale hybrid attention network for sleep stage classification . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2023 , 61 (9) , 2291-2303 . |
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Abstract ：
Vehicle passing angles are critical metrics for evaluating the geometric passability of vehicles. The accurate measurement of these angles is essential for route planning in complex terrain and in guiding the production of specialized vehicles. However, the current measurement methods cannot meet the requirements of efficiency, convenience and robustness. This paper presents a novel measurement method by building and measuring the point cloud of a vehicle chassis. Based on this method, a novel measurement system is designed and its effectiveness is verified. In the system, a wheeled robot acquires and processes data after passing underneath the vehicle. Then, we introduce a new approach to reduce the main sources of error when building point clouds beneath the vehicle, achieved by modifying the extraction algorithm and the proportion of different feature points in each frame. Additionally, we present a fast geometric calculation algorithm for calculating the passing angles. The simulation experiment results demonstrate deviations of 0.06252%, 0.01575%, and 0.003987% when comparing the calculated angles to those of the simulated vehicle. The experimental results show that the method and system are effective at acquiring the point cloud of the vehicle and calculating the parameters of passing angles with good data consistency, exhibiting variances of 0.12407, 0.12407, and 0.69804.
Keyword ：
passing angles passing angles information processing information processing LIDAR LIDAR data acquisition data acquisition point cloud point cloud
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| GB/T 7714 | Chen, Jiaping , Jia, Kebin , Wang, Zhiju et al. An Intelligent Measurement Method and System for Vehicle Passing Angles [J]. | APPLIED SCIENCES-BASEL , 2023 , 13 (11) . |
| MLA | Chen, Jiaping et al. "An Intelligent Measurement Method and System for Vehicle Passing Angles" . | APPLIED SCIENCES-BASEL 13 . 11 (2023) . |
| APA | Chen, Jiaping , Jia, Kebin , Wang, Zhiju , Sun, Zhonghua . An Intelligent Measurement Method and System for Vehicle Passing Angles . | APPLIED SCIENCES-BASEL , 2023 , 13 (11) . |
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Abstract ：
Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.
Keyword ：
data augmentation data augmentation remote sensing remote sensing image segmentation image segmentation deep learning deep learning multi-scale features extraction multi-scale features extraction
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| GB/T 7714 | He, Yan , Jia, Kebin , Wei, Zhihao . Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique [J]. | REMOTE SENSING , 2023 , 15 (9) . |
| MLA | He, Yan et al. "Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique" . | REMOTE SENSING 15 . 9 (2023) . |
| APA | He, Yan , Jia, Kebin , Wei, Zhihao . Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique . | REMOTE SENSING , 2023 , 15 (9) . |
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Abstract ：
The majority of the optical observations collected via spaceborne optical satellites are corrupted by clouds or haze, restraining further applications of Earth observation; thus, exploring an ideal method for cloud removal is of great concern. In this paper, we propose a novel probabilistic generative model named sequential-based diffusion models (SeqDMs) for the cloud-removal task in a remote sensing domain. The proposed method consists of multi-modal diffusion models (MmDMs) and a sequential-based training and inference strategy (SeqTIS). In particular, MmDMs is a novel diffusion model that reconstructs the reverse process of denosing diffusion probabilistic models (DDPMs) to integrate additional information from auxiliary modalities (e.g., synthetic aperture radar robust to the corruption of clouds) to help the distribution learning of main modality (i.e., optical satellite imagery). In order to consider the information across time, SeqTIS is designed to integrate temporal information across an arbitrary length of both the main modality and auxiliary modality input sequences without retraining the model again. With the help of MmDMs and SeqTIS, SeqDMs have the flexibility to handle an arbitrary length of input sequences, producing significant improvements only with one or two additional input samples and greatly reducing the time cost of model retraining. We evaluate our method on a public real-world dataset SEN12MS-CR-TS for a multi-modal and multi-temporal cloud-removal task. Our extensive experiments and ablation studies demonstrate the superiority of the proposed method on the quality of the reconstructed samples and the flexibility to handle arbitrary length sequences over multiple state-of-the-art cloud removal approaches.
Keyword ：
cloud removal cloud removal diffusion models diffusion models multi-modal multi-modal synthetic aperture radar (SAR)-optical synthetic aperture radar (SAR)-optical multi-temporal multi-temporal
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| GB/T 7714 | Zhao, Xiaohu , Jia, Kebin . Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models [J]. | REMOTE SENSING , 2023 , 15 (11) . |
| MLA | Zhao, Xiaohu et al. "Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models" . | REMOTE SENSING 15 . 11 (2023) . |
| APA | Zhao, Xiaohu , Jia, Kebin . Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models . | REMOTE SENSING , 2023 , 15 (11) . |
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Abstract ：
The Three-Dimensional High Efficiency Video Coding standard is a video compression standard developed based on the two-dimensional video coding standard HEVC and used to encode multi-view plus depth format video. This paper proposes an algorithm based on eXtreme Gradient Boosting to solve the problem of high inter-frame coding complexity in 3D-HEVC. Firstly, explore the correlation between the division depth of the inter-frame coding unit and the texture features in the map, as well as the correlation between the coding unit division structure between each map and each viewpoint. After that, based on the machine learning method, a fast selection mechanism for dividing the depth range of the inter-frame coding tree unit based on the eXtreme Gradient Boosting algorithm is constructed. Experimental results show that, compared with the reference software HTM-16.0, this method can save an average of 35.06% of the coding time, with negligible degradation in terms of coding performance. In addition, the proposed algorithm has achieved different degrees of improvement in coding performance compared with the related works.
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| GB/T 7714 | Zhang, Ruyi , Jia, Kebin , Yu, Yuan et al. Fast 3D-HEVC inter coding using data mining and machine learning [J]. | IET IMAGE PROCESSING , 2022 , 16 (11) : 3067-3084 . |
| MLA | Zhang, Ruyi et al. "Fast 3D-HEVC inter coding using data mining and machine learning" . | IET IMAGE PROCESSING 16 . 11 (2022) : 3067-3084 . |
| APA | Zhang, Ruyi , Jia, Kebin , Yu, Yuan , Liu, Pengyu , Sun, Zhonghua . Fast 3D-HEVC inter coding using data mining and machine learning . | IET IMAGE PROCESSING , 2022 , 16 (11) , 3067-3084 . |
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Abstract ：
Three Dimensional-High Efficiency Video Coding ( 3D-HEVC) standard is the latest ThreeDimensional (3D) video coding standard, but the coding complexity increases greatly due to the introduction of depth map coding technology. Among them, the quad-tree partition of depth map intra-frame Coding Unit (CU) accounts for more than 90% of the coding complexity in 3D-HEVC. Therefore, for the intra-frame coding of depth map in 3D-HEVC, considering the high complexity of CU quad-tree partition, a fast prediction scheme of CU partition structure based on deep learning is proposed. Firstly, the dataset of CU partition structure information for learning depth map is constructed. Secondly, a Multi- Branch Convolutional Neural Network (MB-CNN) model for predicting the CU partition structure is built. Then, the MB-CNN model is trained by using the built dataset. Finally, the MB- CNN model is embedded into the 3D-HEVC test platform, which reduces greatly the complexity of CU partition by predicting the partition structure of CU in depth map intraframe coding. Experimental results show that the proposed algorithm reduces effectively the coding complexity of 3D-HEVC without significant synthesized view quality distortion. Specifically, compared to the standard method, the coding complexity on the standard test sequence is reduced by 37.4%.
Keyword ：
Three Dimensional-High Efficiency Video Coding(3D-HEVC) Three Dimensional-High Efficiency Video Coding(3D-HEVC) Coding Unit (CU) partition Coding Unit (CU) partition Intra-frame coding Intra-frame coding Depth map Depth map Deep learning Deep learning
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| GB/T 7714 | Liu Chang , Jia Kebin , Liu Pengyu . Fast Partition Algorithm in Depth Map Intra-frame Coding Unit Based on Multi-branch Network [J]. | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY , 2022 , 44 (12) : 4357-4366 . |
| MLA | Liu Chang et al. "Fast Partition Algorithm in Depth Map Intra-frame Coding Unit Based on Multi-branch Network" . | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY 44 . 12 (2022) : 4357-4366 . |
| APA | Liu Chang , Jia Kebin , Liu Pengyu . Fast Partition Algorithm in Depth Map Intra-frame Coding Unit Based on Multi-branch Network . | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY , 2022 , 44 (12) , 4357-4366 . |
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Abstract ：
3维高效视频编码(3D-HEVC)标准是最新的3维(3D)视频编码标准，但由于其引入深度图编码技术导致编码复杂度大幅增加。其中，深度图帧内编码单元(CU)的四叉树划分占3D-HEVC编码复杂度的90%以上。对此，在3D-HEVC深度图帧内编码模式下，针对CU四叉树划分复杂度高的问题，该文提出一种基于深度学习的CU划分结构快速预测方案。首先，构建学习深度图CU划分结构信息的数据集；其次，搭建预测CU划分结构的多分支卷积神经网络(MB-CNN)模型，并利用构建的数据集训练MB-CNN模型；最后，将MB-CNN模型嵌入3D-HEVC的测试平台，通过直接预测深度图帧内编码模式下CU的划分结构来降低CU划分复杂度。与标准算法相比，编码复杂度平均降低了37.4%。实验结果表明，在不影响合成视点质量的前提下，该文所提算法有效地降低了3D-HEVC的编码复杂度。
Keyword ：
深度学习 深度学习 帧内编码 帧内编码 3维高效视频编码 3维高效视频编码 深度图 深度图 编码单元划分 编码单元划分
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| GB/T 7714 | 刘畅 , 贾克斌 , 刘鹏宇 . 基于多分支网络的深度图帧内编码单元快速划分算法 [J]. | 电子与信息学报 , 2022 : 1-10 . |
| MLA | 刘畅 et al. "基于多分支网络的深度图帧内编码单元快速划分算法" . | 电子与信息学报 (2022) : 1-10 . |
| APA | 刘畅 , 贾克斌 , 刘鹏宇 . 基于多分支网络的深度图帧内编码单元快速划分算法 . | 电子与信息学报 , 2022 , 1-10 . |
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Abstract ：
视频帧类型决策是影响视频编码效率的关键因素之一。为提升x265视频编码器的编码性能，该文提出基于局部亮度直方图的自适应视频帧类型决策算法。首先，在64×64大小的编码树单元(CTU)级别上统计各帧局部亮度直方图，用帧间局部亮度直方图差异表征帧间场景变换程度；其次，引入帧内编码帧(I帧)检测窗，在检测窗内通过比较帧间场景变换程度自适应确定I帧；最后，根据帧间场景变换程度与迷你图像组(MiniGOP)大小之间的相关性确定MiniGOP大小，从而自适应确定普通P和B帧(GPB帧)及双向预测编码帧(B帧)。实验结果表明，与x265标准中的相关算法相比，所提算法能够有效降低x265的编码复杂度，可在减少近5%编码时间的前提下，实现视频I帧、GPB帧和B帧的高效自适应决策。
Keyword ：
局部亮度直方图 局部亮度直方图 视频编码 视频编码 视频帧类型决策 视频帧类型决策 x265 x265
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| GB/T 7714 | 刘鹏宇 , 张悦 , 贾克斌 et al. 基于局部亮度直方图的自适应视频帧类型决策算法 [J]. | 电子与信息学报 , 2022 : 1-8 . |
| MLA | 刘鹏宇 et al. "基于局部亮度直方图的自适应视频帧类型决策算法" . | 电子与信息学报 (2022) : 1-8 . |
| APA | 刘鹏宇 , 张悦 , 贾克斌 , 段堃 , 刘畅 , 孙萱 et al. 基于局部亮度直方图的自适应视频帧类型决策算法 . | 电子与信息学报 , 2022 , 1-8 . |
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