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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

IEEE Transactions on Circuits and Systems for Video technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Advanced Scale-aware Attention for Enhanced Tiny Object Detection in Remote Sensing 2

Advanced Scale-aware Attention for Enhanced Tiny Object Dete...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Qiao, Tingting Chen, He Chai, Bingqian Liu, Wenchao Qi, Guijie Xie, Yizhuang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Recent advancements in remote sensing object detection have progressed, but distinguishing objects from complex backgrounds remains difficult, especially for tiny objects. To address this, we introduce the Advanced Scale-Aware Attention (ASA) module, which retains key channel information while minimizing computational cost. This module reorganizes channels into the batch dimension and splits them into multiple sub-features. The ASA module contains three branches: the first encodes weights based on scale and channel relationships. The second and third branches use different convolution kernel sizes to capture features across various receptive fields. These branches collaborate to enhance pixel-level relationships, improving foreground-background contrast. On the AI-TOD dataset, our method improved AP50 by 8.6% over state-of-the-art approaches. © 2024 IEEE.

关键词： Object recognition

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A Temporal Knowledge Graph Embedding Model Based on Variable Translation

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Tsinghua Science and technology 2024年第5期29卷 1554-1565页

作者： Yadan Han Guangquan Lu Shichao Zhang Liang Zhang Cuifang Zou Guoqiu Wen Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilin 541004China and also with Guangxi Key Lab of Multi-Source Information Mining and SecurityGuangxi Normal UniversityGuilin 541004China

Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous *** is popularly used in knowledge graph completion(or link prediction)***-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)***,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet ***,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship ***,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge *** conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and *** model outperforms baseline models on multiple evaluation metrics according to the experimental results.

关键词： knowledge graph knowledge graph completion variable translation temporal properties link prediction triplet classification

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scGAT: A Cell-Type Annotation Framework for Single-Cell Transcriptomics Using Graph Attention Network and Meta Learning 1

scGAT: A Cell-Type Annotation Framework for Single-Cell Tran...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Dong, Jiayi Li, Ming Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a valuable tool for classifying cell types across various species, tissues, and environmental conditions, thereby advancing the field of life sciences. Automatic cell annotation plays a crucial role in bioinformatics, addressing the challenges associated with time-consuming and labor-intensive manual cell annotation. Although numerous cell annotation algorithms exist, their performance is often constrained by the quality of reference data and inherent technical noise in biological experiments. To address these challenges, we propose the integration of a heterogeneous graph neural network into our cell annotation model, named scGAT. This integration leverages the intricate relationships between expressed genes and cells while disregarding unexpressed genes. Additionally, we introduce a novel approach that utilizes prototype cells to mitigate technical noise. However, the use of prototype cells may lead to limitations in sample size, prompting us to employ meta-learning techniques to determine the model's initial parameters. To assess the effectiveness of scGAT in cell annotation tasks, we conducted comparative analyses using real datasets against other models. The experimental results clearly demonstrate that scGAT significantly outperforms alternative methods. © 2023 IEEE.

关键词： Bioinformatics

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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple *** decomposes condition monitoring signals after coarse-grained processing in varying *** this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single *** the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the *** adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Fault diagnosis Condition monitoring Noise Signal processing algorithms Aerospace electronics Rail transportation Robustness

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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GROD: Joint Inference of Gene Regulatory Networks and Data Imputation in Single-Cell RNA Sequencing with Temporal Consideration

GROD: Joint Inference of Gene Regulatory Networks and Data I...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Dong, Jiayi Wang, Fei Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350337488

Since single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular dynamics, the construction of gene relationships based on dynamic information has attracted much attention. However, the sparsity and dropout events inherent in scRNA-seq data present challenges for downstream analysis like Gene Regulatory Network (GRN) inference. Existing data imputation methods have yielded good results beneficial for cell clustering, but it does not adequately consider gene expression dynamics. To address this, we introduce GROD, a novel deep learning approach that simultaneously infers GRN and imputes scRNA-seq data from a temporal perspective. GROD consists of three key components: an encoder, a graph learner, and a decoder. Experimental results demonstrate that GROD outperforms existing methods in both GRN inference and data imputation tasks, providing superior accuracy in capturing gene regulatory relationships and accurately imputing missing values. By integrating these two tasks, GROD enables more accurate downstream analysis and facilitates deeper insights into cellular dynamics. © 2023 IEEE.

关键词： cellular dynamics data imputation gene regulatory network scRNA-seq

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DCSN: A Flexible and Efficient Lightweight Network for Dense Cell Segmentation 1

DCSN: A Flexible and Efficient Lightweight Network for Dense...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Pan, Futai Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

Cells serve as the fundamental building blocks of living organisms and necessitate the utilization of diverse microscopic imaging techniques for observation. Nevertheless, there exist numerous limitations within phase-contrast microscopy imaging technology, leading to indistinct imaging in a considerable number of acquired cell images. Furthermore, the wide range of cell shapes and sizes, often densely distributed, poses challenges for research in the field of cell image analysis. We propose Dense Cell Segmentation Network (DCSN), an anchor-based single-stage instance segmentation method, to address the challenge of dense detection and instance segmentation in high-density cell images. While methods based on transformers have emerged as prominent approaches in natural image detection and segmentation, they are limited in their applicability to cell image analysis due to the scarcity of cell image datasets. Consequently, we aim to introduce a simple yet effective instance segmentation method to further address the challenge of cell image segmentation. DCSN is a lightweight multi-task model, combining object detection and semantic segmentation to achieve accurate instance segmentation. We validate our approach on LIVECell, a publicly available annotated cell dataset, and demonstrate its efficacy. By combining the advantages of anchor-based methods and single-stage methods, DCSN achieves a favorable balance between speed and performance. In comparison to the anchor-based two-stage model Mask R-CNN, DCSN offers a streamlined process and faster inference speed. Compared to the anchor-free single-stage method, CenterMask, our model demonstrates superior segmentation performance and generalization capabilities. Moreover,DCSN is a general end-to-end model that can be applied to various domains beyond cell segmentation tasks. © 2023 IEEE.

关键词： Object detection

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Audio-guided self-supervised learning for disentangled visual speech representations

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Frontiers of Computer Science 2024年第6期18卷 277-279页

作者： Dalu FENG Shuang YANG Shiguang SHAN Xilin CHEN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

1 *** visual speech representations from talking face videos is an important problem for several speech-related tasks,such as lip reading,talking face generation,and audiovisual speech separation[1,2].The key difficulty lies in tackling speech-irrelevant factors presented in the videos,such as lighting,resolution,viewpoints,and head motion.

关键词： such dis visual

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Prominent Edge Characteristics for Open World Object Detection 17

Prominent Edge Characteristics for Open World Object Detecti...

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17th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Li, Shihao Liu, Feng Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing China

ISBN: (纸本)9798331507398

Open world object detection aims to simulate the human process of recognizing objects in real life scenarios. It can identify categories not introduced during the training phase as unknown during the testing phase and recognize these unknowns in subsequent incremental learning. This capability is beyond the reach of traditional object detection, making it closer to real world human experiences and thus garnering significant attention from researchers. However, existing studies largely focus on generating better pseudo labels to improve the recall rate of unknown objects, which evidently neglects the intrinsic differences between objects and backgrounds. Considering important information such as the visual saliency and edge features of objects in realistic scenes, this paper proposes a novel solution called PEC, which directly extracts objects from their background to detect known and unknown objects more accurately. Comprehensive experiments on the M-OWODB benchmark demonstrate that our method outperforms other current mainstream approaches. © 2024 IEEE.

关键词： edge attention object detection open world unknown object recognition

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