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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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3D Hand Pose Estimation Using Semantic Dynamic Hypergraph Convolutional Networks

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Journal of Shanghai Jiaotong University (Science) 2024年 1-11页

作者： Wu, Yalei Li, Jinghua Kong, Dehui Li, Qianxing Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing University of Technology Beijing100124 China

Due to self-occlusion and high degree of freedom, estimating 3D hand pose from a single RGB image is a great challenging problem. Graph convolutional networks (GCNs) use graphs to describe the physical connection relationships between hand joints and improve the accuracy of 3D hand pose regression. However, GCNs cannot effectively describe the relationships between non-adjacent hand joints. Recently, hypergraph convolutional networks (HGCNs) have received much attention as they can describe multi-dimensional relationships between nodes through hyperedges;therefore, this paper proposes a framework for 3D hand pose estimation based on HGCN, which can better extract correlated relationships between adjacent and non-adjacent hand joints. To overcome the shortcomings of predefined hypergraph structures, a kind of dynamic hypergraph convolutional network is proposed, in which hyperedges are constructed dynamically based on hand joint feature similarity. To better explore the local semantic relationships between nodes, a kind of semantic dynamic hypergraph convolution is proposed. The proposed method is evaluated on publicly available benchmark datasets. Qualitative and quantitative experimental results both show that the proposed HGCN and improved methods for 3D hand pose estimation are better than GCN, and achieve state-of-the-art performance compared with existing methods. © 2024, Shanghai Jiao Tong University.

关键词： Convolution

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An improved description method of the bumpy texture

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Science in China(Series F) 2009年第3期52卷 523-528页

作者： GAO RongHua KONG DeHui YIN BaoCai Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Beijing University of Technology Beijing 100124 China

Bump mapping is a texture-based rendering approach for simulating surface details to make its illumination results have three-dimensional effects. The bumpy properties of an object are determined by height maps. But in the process of generating height maps, a problem arises, i.e. to get a correct value of the pixel height, empirical data should be calculated repeatedly, which proves very complicated, and meanwhile the realistic rendering effect is reduced, because the bumpy property is exaggerated in the height map. Therefore, in this paper, we present a method for describing the details of the bumpy texture, where a new concept ＂bumpy map＂ is introduced to replace the height map. Experimental results demonstrate that the bumpy details produced by the ＂bumpy map＂ are more consistent with the original bumpy texture than by the method of height map.

关键词： bump mapping bumpy texture height map bumpy map

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Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1587-1604页

作者： Jinduo Liu Jihao Zhai Junzhong Ji Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijingChina

Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted considerable attention within the bioinformatics ***,Bayesian network(BN)techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell ***,current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells.A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony(PDABC),named ***,PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 *** experimental results on several simulated datasets,as well as a previously published multi-parameter fluorescence-activated cell sorter dataset,indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.

关键词： Bayesian network causal protein signaling networks parallel discrete artificial bee colony single-cell data

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Extracting hand articulations from monocular depth images using curvature scale space descriptors

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Frontiers of Information technology & Electronic Engineering 2016年第1期17卷 41-54页

作者： Shao-fan WANG Chun LI De-hui KONG Bao-cai YIN Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Metropolitan Transportation Beijing University of Technology Beijing 100124 China School of Software Technology Dalian University of Technology Dalian 116024 China Collaborative Innovation Center of Electric Vehicles in Beijing Beijing 100081 China

We propose a framework of hand articulation detection from a monocular depth image using curvature scale space(CSS) descriptors. We extract the hand contour from an input depth image, and obtain the fingertips and finger-valleys of the contour using the local extrema of a modified CSS map of the contour. Then we recover the undetected fingertips according to the local change of depths of points in the interior of the contour. Compared with traditional appearance-based approaches using either angle detectors or convex hull detectors, the modified CSS descriptor extracts the fingertips and finger-valleys more precisely since it is more robust to noisy or corrupted data;moreover, the local extrema of depths recover the fingertips of bending fingers well while traditional appearance-based approaches hardly work without matching models of hands. Experimental results show that our method captures the hand articulations more precisely compared with three state-of-the-art appearance-based approaches.

关键词： Curvature scale space (CSS), Hand articulation, Convex hull, Hand contour

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DTCC:Multi-level dilated convolution with transformer for weakly-supervised crowd counting

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Computational Visual Media 2023年第4期9卷 859-873页

作者： Zhuangzhuang Miao Yong Zhang Yuan Peng Haocheng Peng Baocai Yin Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Taiji Computer Corporation Ltd. China

Crowd counting provides an important foundation for public security and urban *** to the existence of small targets and large density variations in crowd images,crowd counting is a challenging *** methods usually apply convolution neural networks(CNNs)to regress a density map,which requires annotations of individual persons and ***-supervised methods can avoid detailed labeling and only require counts as annotations of images,but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually *** propose a weakly-supervised method,DTCC,which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd *** main components include a recursive swin transformer and a multi-level dilated convolution regression *** recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features,including global *** multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction *** module can capture both low-and high-level features simultaneously to enhance the receptive *** addition,two regression head fusion mechanisms realize dynamic and mean fusion *** on four well-known benchmark crowd counting datasets(UCF_CC_50,ShanghaiTech,UCF_QNRF,and JHU-Crowd++)show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.

关键词： crowd counting transformer dilated convolution global perspective field pyramid

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Metro Flow Prediction with Hierarchical Hypergraph Attention Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3012-3021页

作者： Wang, Jingcheng Zhang, Yong Hu, Yongli Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing100124 China

Graph convolutional neural networks, which aim to learn the spatial correlation within multivariate time-series data, have achieved significant advancements in traffic prediction. Despite the proliferation of various spatiotemporal graph neural networks, a fundamental question of the predefined graphs still remain to be explored: how can the graph be dynamically optimized as time shifts? Especially for tasks such as rail transit where travel patterns of passengers change periodically. In other words, the spatial correlation of multivariate traffic time-series data is closely intertwined with temporal features, which are tricky to reflect through static graphs with handcrafted association. To adaptively learn the spatial correlation in feature domain, we propose hierarchical hypergraph attention networks (HHGATs). The hierarchical nature of the framework is reflected in both temporal and spatial dimensions. At different time spans, the model explores the hidden hyperedges with a local hypergraph attention mechanism and optimizes the hypergraph with a global attention mechanism. The nodelevel prediction results are then fused after the downstream tasks of spatiotemporal hypergraph convolutional networks. The effectiveness and accuracy of the proposed model are evaluated on historical datasets of Beijing and Hangzhou. Compared to the baselines, the proposed approach consistently demonstrates superior performance in various evaluation scenarios. © 2020 IEEE.

关键词： Forecasting

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Cross-media Hot Topic Auto-tracking Model Based on Semantics and Temporal Context

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Chinese Journal of Electronics 2015年第3期24卷 529-534页

作者： LIANG Meiyu DU Junping ZHOU Yipeng Beijing Key Lab of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications School of Computer and Information Engineering Beijing Technology and Business University

As the Internet multimedia information grows explosively, seeking an automatic technology to realize the effective organization and management of crossmedia emergency information is significantly necessary. A novel cross-media hot topic auto-tracking model based on semantics and temporal context is proposed in this paper. According to the semantic correlations of cross-media information, we learn the image visual semantics by the text semantics based on the Latent Dirichlet Allocation probability model, and establish the unified cross-media information description on the same semantic level. Also a semantics-based two-step feature dimension-reduction scheme is proposed to establish the efficient semantic feature space. The self-adaptive learning of topic model is realized to track the dynamic changes in the topic. Experimental results demonstrate that the proposed method outperforms the existing methods, which further improves the effect of hot topic auto-tracking.

关键词： Cross-media data mining Topic autotracking Semantic model Latent dirichlet allocation(LDA) Temporal context

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Multi-lighting 3D face morphable model based on mesh resampling

Multi-lighting 3D face morphable model based on mesh resampl...

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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Hu, Yongli Yin, Baocai Sun, Yanfeng Multimedia and Intelligent Software Technology Laboratory Beijing University of Technology Beijing 100022 China

ISBN: (纸本)0780388747

This work describes a multi-lighting 3D face morphable model based on mesh resampling. Despite having realistic results and modeling automatically in 3D face synthesis, the morphable model depends on the unstable optical flow algorithm for model construction, and the model matching is not fitting for complex illumination. To improve modeling results, the mesh resampling method is used to overcome the key problem of model construction, the pixel-to-pixel alignments of prototypic faces. A multi-lighting model is also proposed to evaluate the illumination of the input facial image. The experimental results show these measurements have good performance. © 2005 IEEE.

关键词： Face recognition

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File format for storage of scalable video

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第5期7卷 706-712页

作者： BAI Gang SUN Xiao-yan WU Feng YIN Bao-cai LI Shi-peng Beijing Municipal Multimedia and Intelligent Software Key Laboratory Beijing University of Technology Beijing 100022 China Microsoft Research Asia Beijing 100086 China Microsoft Research Asia Beijing 100086 China Beijing Municipal Multimedia and Intelligent Software Key Laboratory Beijing University of Technology Beijing 100022 China Microsoft Research Asia Beijing 100086 China

A file format for storage of scalable video is proposed. A generic model is presented to enable a codec independent description of scalable video stream. The relationships, especially the dependencies, among sub-streams in a scalable video stream are specified sufficiently and effectively in the proposed model. Complying with the presented scalable video stream model, the file format for scalable video is proposed based on ISO Base Media File Format, which is simple and flexible enough to address the demands of scalable video application as well as the non-scalable ones.

关键词： Codes (standards) Data structures Digital storage Internet Mathematical models multimedia systems

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