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学者姓名：于元隆
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为实现实验室科研管理过程中的成员工时和工效分析、任务分配的合理性评估等需求,研究一种基于摄像头视频、考勤机记录、Web系统记录等的多模态工效分析系统MASRE.该系统通过实验室科研人员工时及其玩手机行为导致的无效工时、工效实时对比与展示,激励实验室成员投入更多的时间开展学术研究.依据系统计算的工效变化趋势,实验室负责人可分析科研任务分配的合理性,科研人员也可分析影响其科研效率的因素.MASRE系统由负责工时工效统计的Web系统模块和支持无效工时自动识别的AI分析模块构成,采用PyTorch、VUE 3、MySQL等技术实现.以该系统研发及其研究报告撰写的工时工效分析为例进行实验分析,结果表明MASRE系统可有效识别无效工时并进行工时统计与工效分析.同时,该系统已免费向实验室研究团队开放申请注册使用,网址为 .
Keyword ：
任务分配 任务分配 多模态采样 多模态采样 检测方法 检测方法 注意力机制 注意力机制 玩手机行为识别 玩手机行为识别 科研团队 科研团队 科研工效分析 科研工效分析
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| GB/T 7714 | 廖龙龙 , 郑志伟 , 张煜朋 et al. 基于多模态的实验室科研工效分析系统 [J]. | 计算机系统应用 , 2024 , 33 (1) : 68-75 . |
| MLA | 廖龙龙 et al. "基于多模态的实验室科研工效分析系统" . | 计算机系统应用 33 . 1 (2024) : 68-75 . |
| APA | 廖龙龙 , 郑志伟 , 张煜朋 , 方鑫 , 郑育强 , XIONG Ning et al. 基于多模态的实验室科研工效分析系统 . | 计算机系统应用 , 2024 , 33 (1) , 68-75 . |
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Ultra-high resolution image segmentation poses a formidable challenge for UAVs with limited computation resources. Moreover, with multiple deployed tasks (e.g., mapping, localization, and decision making), the demand for a memory efficient model becomes more urgent. This letter delves into the intricate problem of achieving efficient and effective segmentation of ultra-high resolution UAV imagery, while operating under stringent GPU memory limitation. To address this problem, we propose a GPU memory-efficient and effective framework. Specifically, we introduce a novel and efficient spatial-guided high-resolution query module, which enables our model to effectively infer pixel-wise segmentation results by querying nearest latent embeddings from low-resolution features. Additionally, we present a memory-based interaction scheme with linear complexity to rectify semantic bias beneath the high-resolution spatial guidance via associating cross-image contextual semantics. For evaluation, we perform comprehensive experiments over public benchmarks under both conditions of small and large GPU memory usage limitations. Notably, our model gains around 3% advantage against SOTA in mIoU using comparable memory. Furthermore, we show that our model can be deployed on the embedded platform with less than 8 G memory like Jetson TX2.
Keyword ：
Aerial Systems: Perception and Autonomy Aerial Systems: Perception and Autonomy Autonomous aerial vehicles Autonomous aerial vehicles Deep Learning for Visual Perception Deep Learning for Visual Perception Graphics processing units Graphics processing units Image resolution Image resolution Memory management Memory management Semantics Semantics Semantic segmentation Semantic segmentation Spatial resolution Spatial resolution
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| GB/T 7714 | Li, Qi , Cai, Jiaxin , Luo, Jiexin et al. Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2024 , 9 (2) : 1708-1715 . |
| MLA | Li, Qi et al. "Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery" . | IEEE ROBOTICS AND AUTOMATION LETTERS 9 . 2 (2024) : 1708-1715 . |
| APA | Li, Qi , Cai, Jiaxin , Luo, Jiexin , Yu, Yuanlong , Gu, Jason , Pan, Jia et al. Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2024 , 9 (2) , 1708-1715 . |
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With the growing significance of data privacy protection, Source-Free Domain Adaptation (SFDA) has gained attention as a research topic that aims to transfer knowledge from a labeled source domain to an unlabeled target domain without accessing source data. However, the absence of source data often leads to model collapse or restricts the performance improvements of SFDA methods, as there is insufficient true-labeled knowledge for each category. To tackle this, Source-Free Active Domain Adaptation (SFADA) has emerged as a new task that aims to improve SFDA by selecting a small set of informative target samples labeled by experts. Nevertheless, existing SFADA methods impose a significant burden on human labelers, requiring them to continuously label a substantial number of samples throughout the training period. In this paper, a novel approach is proposed to alleviate the labeling burden in SFADA by only necessitating the labeling of an extremely small number of samples on a one-time basis. Moreover, considering the inherent sparsity of these selected samples in the target domain, a Self-adaptive Clustering-based Active Learning (SCAL) method is proposed that propagates the labels of selected samples to other datapoints within the same cluster. To further enhance the accuracy of SCAL, a self-adaptive scale search method is devised that automatically determines the optimal clustering scale, using the entropy of the entire target dataset as a guiding criterion. The experimental evaluation presents compelling evidence of our method's supremacy. Specifically, it outstrips previous SFDA methods, delivering state-of-the-art (SOTA) results on standard benchmarks. Remarkably, it accomplishes this with less than 0.5% annotation cost, in stark contrast to the approximate 5% required by earlier techniques. The approach thus not only sets new performance benchmarks but also offers a markedly more practical and cost-effective solution for SFADA, making it an attractive choice for real-world applications where labeling resources are limited. We propose a novel approach to alleviate the labeling burden in SFADA by only necessitating the labeling of an extremely small number of samples on a one-time basis. Moreover, considering the inherent sparsity of these selected samples in the target domain, we propose a Self-adaptive Clustering-based Active Learning (SCAL) method that propagates the labels of selected samples to other datapoints within the same cluster. To further enhance the accuracy of SCAL, we devise an self-adaptive scale search method that automatically determines the optimal clustering scale, using the entropy of the entire target dataset as a guiding criterion.image
Keyword ：
computer vision computer vision image recognition image recognition
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| GB/T 7714 | Sun, Zhishu , Lin, Luojun , Yu, Yuanlong . You only label once: A self-adaptive clustering-based method for source-free active domain adaptation [J]. | IET IMAGE PROCESSING , 2024 , 18 (5) : 1268-1282 . |
| MLA | Sun, Zhishu et al. "You only label once: A self-adaptive clustering-based method for source-free active domain adaptation" . | IET IMAGE PROCESSING 18 . 5 (2024) : 1268-1282 . |
| APA | Sun, Zhishu , Lin, Luojun , Yu, Yuanlong . You only label once: A self-adaptive clustering-based method for source-free active domain adaptation . | IET IMAGE PROCESSING , 2024 , 18 (5) , 1268-1282 . |
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Domain Generalization (DG) aims to generalize a model trained on multiple source domains to an unseen target domain. The source domains always require precise annotations, which can be cumbersome or even infeasible to obtain in practice due to the vast amount of data involved. Web data, namely web -crawled images, offers an opportunity to access large amounts of unlabeled images with rich style information, which can be leveraged to improve DG. From this perspective, we introduce a novel paradigm of DG, termed as Semi -Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close -set and open -set SSDG. The close -set SSDG is based on existing public DG datasets, while the open -set SSDG, built on the newly -collected web -crawled datasets, presents a novel yet realistic challenge that pushes the limits of current technologies. A natural approach of SSDG is to transfer knowledge from labeled data to unlabeled data via pseudo labeling, and train the model on both labeled and pseudo -labeled data for generalization. Since there are conflicting goals between domain -oriented pseudo labeling and out -of -domain generalization, we develop a pseudo labeling phase and a generalization phase independently for SSDG. Unfortunately, due to the large domain gap, the pseudo labels provided in the pseudo labeling phase inevitably contain noise, which has negative affect on the subsequent generalization phase. Therefore, to improve the quality of pseudo labels and further enhance generalizability, we propose a cyclic learning framework to encourage a positive feedback between these two phases, utilizing an evolving intermediate domain that bridges the labeled and unlabeled domains in a curriculum learning manner. Extensive experiments are conducted to validate the effectiveness of our method. It is worth highlighting that web -crawled images can promote domain generalization as demonstrated by the experimental results.
Keyword ：
Domain generalization Domain generalization Semi-supervised learning Semi-supervised learning Transfer learning Transfer learning Unsupervised domain adaptation Unsupervised domain adaptation
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| GB/T 7714 | Lin, Luojun , Xie, Han , Sun, Zhishu et al. Semi-supervised domain generalization with evolving intermediate domain [J]. | PATTERN RECOGNITION , 2024 , 149 . |
| MLA | Lin, Luojun et al. "Semi-supervised domain generalization with evolving intermediate domain" . | PATTERN RECOGNITION 149 (2024) . |
| APA | Lin, Luojun , Xie, Han , Sun, Zhishu , Chen, Weijie , Liu, Wenxi , Yu, Yuanlong et al. Semi-supervised domain generalization with evolving intermediate domain . | PATTERN RECOGNITION , 2024 , 149 . |
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Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel -level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug -and -play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty -related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state -of -the -arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.
Keyword ：
dynamic convolution dynamic convolution facial beauty prediction facial beauty prediction kernel attention kernel attention
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| GB/T 7714 | Sun, Zhishu , Xiao, Zilong , Yu, Yuanlong et al. Dynamic Attentive Convolution for Facial Beauty Prediction [J]. | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS , 2024 , E107 (2) : 239-243 . |
| MLA | Sun, Zhishu et al. "Dynamic Attentive Convolution for Facial Beauty Prediction" . | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E107 . 2 (2024) : 239-243 . |
| APA | Sun, Zhishu , Xiao, Zilong , Yu, Yuanlong , Lin, Luojun . Dynamic Attentive Convolution for Facial Beauty Prediction . | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS , 2024 , E107 (2) , 239-243 . |
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Feature selection is a commonly utilized methodology in multi-label learning (MLL) for tackling the challenge of high-dimensional data. Accurate annotation of relevant labels is crucial for successful multi-label feature selection (MFS). Nevertheless, multi-label datasets frequently consist of ground-truth and noisy labels in real-world applications, giving rise to the partial multi-label learning (PML) problem. The inclusion of noisy labels complicates the task of conventional MFS methods in accurately identifying the optimal features subset in such datasets. To tackle this issue, we propose a novel partial multi-label feature selection method with low-rank sparse factorization and manifold learning, called PMFS-LRS. Specifically, we first decompose the candidate label matrix into two distinct components: a low-rank matrix referring to ground-truth labels and a sparse matrix referring to noisy labels. This decomposition allows PMFS-LRS to effectively distinguish noise labels from ground-truth labels, thereby mitigating the impact of noisy data. Then, the local label correlations are explored using a manifold learning framework to improve the label disambiguation performance. Finally, a l2,1-norm regularization is integrated into the objective function to facilitate effective feature selection. Comprehensive experiments conducted on both real-world and synthetic PML datasets demonstrate that PMFS-LRS is superior to several existing state-of-the-art MFS methods. © 2024 Elsevier B.V.
Keyword ：
Clustering algorithms Clustering algorithms Feature Selection Feature Selection Learning systems Learning systems Matrix algebra Matrix algebra Matrix factorization Matrix factorization
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| GB/T 7714 | Sun, Zhenzhen , Chen, Zexiang , Liu, Jinghua et al. Partial multi-label feature selection via low-rank and sparse factorization with manifold learning [J]. | Knowledge-Based Systems , 2024 , 296 . |
| MLA | Sun, Zhenzhen et al. "Partial multi-label feature selection via low-rank and sparse factorization with manifold learning" . | Knowledge-Based Systems 296 (2024) . |
| APA | Sun, Zhenzhen , Chen, Zexiang , Liu, Jinghua , Chen, Yewang , Yu, Yuanlong . Partial multi-label feature selection via low-rank and sparse factorization with manifold learning . | Knowledge-Based Systems , 2024 , 296 . |
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Facial beauty prediction (FBP) aims to develop a system to assess facial attractiveness automatically. Through prior research and our own observations, it has become evident that attribute information, such as gender and race, is a key factor leading to the distribution discrepancy in the FBP data. Such distribution discrepancy hinders current conventional FBP models from generalizing effectively to unseen attribute domain data, thereby discounting further performance improvement. To address this problem, in this paper, we exploit the attribute information to guide the training of convolutional neural networks (CNNs), with the final purpose of implicit feature alignment across various attribute domain data. To this end, we introduce the attribute information into convolution layer and batch normalization (BN) layer, respectively, as they are the most crucial parts for representation learning in CNNs. Specifically, our method includes: 1) Attribute -guided convolution (AgConv) that dynamically updates convolutional filters based on attributes by parameter tuning or parameter rebirth; 2) Attribute -guided batch normalization (AgBN) is developed to compute the attribute -specific statistics through an attribute guided batch sampling strategy; 3) To benefit from both approaches, we construct an integrated framework by combining AgConv and AgBN to achieve a more thorough feature alignment across different attribute domains. Extensive qualitative and quantitative experiments have been conducted on the SCUTFBP, SCUT-FBP5500 and HotOrNot benchmark datasets. The results show that AgConv significantly improves the attribute -guided representation learning capacity and AgBN provides more stable optimization. Owing to the combination of AgConv and AgBN, the proposed framework (Ag-Net) achieves further performance improvement and is superior to other state-of-the-art approaches for FBP.
Keyword ：
Batch normalization Batch normalization Dynamic convolution Dynamic convolution Facial attractiveness assessment Facial attractiveness assessment Facial beauty prediction Facial beauty prediction
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| GB/T 7714 | Sun, Zhishu , Lin, Luojun , Yu, Yuanlong et al. Learning feature alignment across attribute domains for improving facial beauty prediction [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 249 . |
| MLA | Sun, Zhishu et al. "Learning feature alignment across attribute domains for improving facial beauty prediction" . | EXPERT SYSTEMS WITH APPLICATIONS 249 (2024) . |
| APA | Sun, Zhishu , Lin, Luojun , Yu, Yuanlong , Jin, Lianwen . Learning feature alignment across attribute domains for improving facial beauty prediction . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 249 . |
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Nighttime semantic segmentation is an important but challenging research problem for autonomous driving. The major challenges lie in the small objects or regions from the under-/over-exposed areas or suffer from motion blur caused by the camera deployed on moving vehicles. To resolve this, we propose a novel hard-class-aware module that bridges the main network for full-class segmentation and the hard-class network for segmenting aforementioned hard-class objects. In specific, it exploits the shared focus of hard-class objects from the dual-stream network, enabling the contextual information flow to guide the model to concentrate on the pixels that are hard to classify. In the end, the estimated hard-class segmentation results will be utilized to infer the final results via an adaptive probabilistic fusion refinement scheme. Moreover, to overcome over-smoothing and noise caused by extreme exposures, our model is modulated by a carefully crafted pretext task of constructing an exposure-aware semantic gradient map, which guides the model to faithfully perceive the structural and semantic information of hard-class objects while mitigating the negative impact of noises and uneven exposures. In experiments, we demonstrate that our unique network design leads to superior segmentation performance over existing methods, featuring the strong ability of perceiving hard-class objects under adverse conditions. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Keyword ：
Classification (of information) Classification (of information) Semantics Semantics Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Liu, Wenxi , Cai, Jiaxin , Li, Qi et al. Learning Nighttime Semantic Segmentation the Hard Way [J]. | ACM Transactions on Multimedia Computing, Communications and Applications , 2024 , 20 (7) . |
| MLA | Liu, Wenxi et al. "Learning Nighttime Semantic Segmentation the Hard Way" . | ACM Transactions on Multimedia Computing, Communications and Applications 20 . 7 (2024) . |
| APA | Liu, Wenxi , Cai, Jiaxin , Li, Qi , Liao, Chenyang , Cao, Jingjing , He, Shengfeng et al. Learning Nighttime Semantic Segmentation the Hard Way . | ACM Transactions on Multimedia Computing, Communications and Applications , 2024 , 20 (7) . |
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Feature selection plays a critical role in many machine learning applications as it effectively addresses the challenges posed by "the curse of dimensionality" and enhances the generalization capability of trained models. However, existing approaches for multi-class feature selection (MFS) often combine sparse regularization with a simple classification model, such as least squares regression, which can result in suboptimal performance. To address this limitation, this paper introduces a novel MFS method called Sparse Softmax Feature Selection ((SFS)-F-2). (SFS)-F-2 combines a l(2,0)-norm regularization with the Softmax model to perform feature selection. By utilizing the l(2,0)-norm, (SFS)-F-2 produces a more precise sparsity solution for the feature selection matrix. Additionally, the Softmax model improves the interpretability of the model's outputs, thereby enhancing the classification performance. To further enhance discriminative feature selection, a discriminative regularization, derived based on linear discriminate analysis (LDA), is incorporated into the learning model. Furthermore, an efficient optimization algorithm, based on the alternating direction method of multipliers (ADMM), is designed to solve the objective function of (SFS)-F-2. Extensive experiments conducted on various datasets demonstrate that (SFS)-F-2 achieves higher accuracy in classification tasks compared to several contemporary MFS methods.
Keyword ：
Alternating direction method of multipliers Alternating direction method of multipliers Discriminative regularization Discriminative regularization L-2,L-0-norm regularization L-2,L-0-norm regularization Multi-class feature selection Multi-class feature selection
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| GB/T 7714 | Sun, Zhenzhen , Chen, Zexiang , Liu, Jinghua et al. Multi-class feature selection via Sparse Softmax with a discriminative regularization [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2024 . |
| MLA | Sun, Zhenzhen et al. "Multi-class feature selection via Sparse Softmax with a discriminative regularization" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2024) . |
| APA | Sun, Zhenzhen , Chen, Zexiang , Liu, Jinghua , Yu, Yuanlong . Multi-class feature selection via Sparse Softmax with a discriminative regularization . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2024 . |
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HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency. IEEE
Keyword ：
Autonomous driving Autonomous driving BEV perception BEV perception Estimation Estimation Feature extraction Feature extraction Layout Layout Roads Roads segmentation segmentation Task analysis Task analysis Three-dimensional displays Three-dimensional displays Transformers Transformers
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| GB/T 7714 | Liu, W. , Li, Q. , Yang, W. et al. Monocular BEV Perception of Road Scenes Via Front-to-Top View Projection [J]. | IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024 , 46 (9) : 1-17 . |
| MLA | Liu, W. et al. "Monocular BEV Perception of Road Scenes Via Front-to-Top View Projection" . | IEEE Transactions on Pattern Analysis and Machine Intelligence 46 . 9 (2024) : 1-17 . |
| APA | Liu, W. , Li, Q. , Yang, W. , Cai, J. , Yu, Y. , Ma, Y. et al. Monocular BEV Perception of Road Scenes Via Front-to-Top View Projection . | IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024 , 46 (9) , 1-17 . |
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