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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yindong Chen, Jie Wang, Li Chen, Miaohong Zhang, Guoming Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Eye Hospital China

ISBN: (纸本)9798350337488

Retinopathy of Prematurity (ROP) is a retina disorder that affects premature infants with lower weights. If the patient cannot get the treatment in time when the illness reaches the last stage, irreversible vision loss will be caused. Nevertheless, there has been relatively little consideration given to the segmentation of the ridge, the key clinical characteristic of the illness. Additionally, existing research has not adequately addressed several segmentation issues, such as fragmentary topology, class imbalance, and false positives. This paper proposes a Densely Dilated U-Net (DD-UNet) improved from U-Net to tackle these challenges. Furthermore, the post-processing techniques based on the spatial relationship between vessels and ridges, along with the relative pixel counts of ridges and false positive results is integrated to mitigate false positive results in the predicted ridge. To enhance the precision of thin vessel, a sliding window sampling method is introduced for refined training. Compared with the state-of-the-art models in medical image segmentation, DD-UNet performs well in curvilinear structure segmentation of fundus image. For instance, our DD-UNet outperforms the Attention U-Net by 6.26% in terms of sensitivity and exhibits a 1.85% higher dice score in ridge segmentation. © 2023 IEEE.

关键词： curvilinear structure segmentation fundus image Retinopathy of Prematurity (ROP) ridge segmentation vessel segmentation

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of computer science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU SKLSDE Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition EM algorithm label aggregation

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Twitter Stance Detection via Neural Production Systems 48

Twitter Stance Detection via Neural Production Systems

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Bowen Ding, Daijun Xu, Guangning Guo, Jinjin Huang, Zhichao Huang, Xu Shenzhen Technology University College of Big Data and Internet Shenzhen China Harbin Institute of Technology Computer Science & Technology Shenzhen China Jd Intelligent Cities Research Beijing China

ISBN: (纸本)9781728163277

Stance detection is an important task, which aims to classify the attitude of an opinionated text toward a given target. In this paper, we develop an interpretable neural production system for stance detection (NPS4SD). NPS4SD is an end-to-end deep learning model, which consists of a set of knowledge rules that are applied by binding with specific entities. NPS4SD consists of two main components: a pretrained model for learning the text representation and a variable binding network (VBN) to bind the knowledge rules with text entities. Extensive experiments are conducted to evaluate the effectiveness of the proposed NPS4SD model on three real-world datasets with in-domain, cross-target and zero-shot setups. Experimental results demonstrate that NPS4SD achieves substantially better performance than the strong competitors for the stance detection task. © 2023 IEEE.

关键词： deep neural network neural production system stance detection

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Text-Video Completion Networks With Motion Compensation And Attention Aggregation

Text-Video Completion Networks With Motion Compensation And ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianan Wang Zhiliang Wu Hanyu Xuan Yan Yan School of Computer Science and Engineering Nanjing University of Science and Technology China CCAI Zhejiang University China School of Big Data and Statistics Anhui University China Department of Computer Science Illinois Institute of Technology USA

The purpose of video inpainting is to fill a specified area with reasonable content. However, in the case of multiple targets and complex textures, current methods struggle to distinguish between feature information of the targets, leading to confusing or fuzzy inpainting results. In this paper, we design a new text-video completion network based on a motion compensation and temporal attention feature aggregation. Our network utilizes information from reference frames and target frames to complete the damaged region of the target frame. We first employ motion compensation to align the features of reference frames, and then use the temporal attention module to aggregate these features, resulting in accurate and reasonable content. To evaluate the effectiveness of our method, we introduce a new text video dataset with multiple text objects and complex textures, presenting a novel and challenging task for inpainting research. Through quantitative and qualitative comparison experiments, we demonstrate that our model outperforms existing baseline models in scenarios with multiple objects and complex textures.

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Recent Trends in Text-to-Image GAN

Recent Trends in Text-to-Image GAN

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Varun Kumar Dharmender Kumar Department of Computer Science and Technology Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Data Science and Artificial Intelligence Guru Jambheshwar University of Science and Technology Hisar Haryana India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Generating genuine images from textual description is challenging for both computer vision and linguistic representation in text-to-image synthesis. Generative adversarial networks (GAN) are an emerging generative model that has been producing great results by generating high-quality images with diverse images. The present review provides an overview of GAN with its background like architecture, game theory key ideas, loss functions, performance metrics and challenges. Recent and relevant text-to-image GAN models are discussed, including Gradual Refinement GAN (GR-GAN), Generative Adversarial CLIPS (GALIP), GigaGAN and StyleGAN-T with their architecture, dataset used highlighting limitations, strengths, year, and applications and comparing their performance metrics like Inception Score (IS) and Fréchet Inception Distance (FID) with identifying future directions, such as drawing boundaries for open research challenges. The present review functions as an extensive knowledge base and is highly valuable for researchers and practitioners who are interested in learning more about text-to-image using GANs.

关键词： Measurement Reviews Knowledge based systems Text to image computer architecture Linguistics Generative adversarial networks

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Multivariate Prediction of Retail Sales by Multi-task Time Series Learning

Multivariate Prediction of Retail Sales by Multi-task Time S...

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2021 International Conference on computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2021

作者： Lin, Miaopei Yu, Jianxing Yin, Jian Sun Yat-sen University School of Computer Science and Engineering Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Predicting retail sales is a hot research topic that can help firms achieve on-demand procurement. That can reduce the extra costs caused by an inventory shortage or surpluses. Traditional methods usually regard the sales of a certain product as the independent time series and then solve the task by a curve fitting model. However, the sales volume is not a single value, but the one aggregated from many types of products. There are correlations between different types of product sales. The increase in one type of product sales may lead to changes in sales in another type. Without capturing the correlation between different product sales, it is difficult to obtain satisfactory performance. To solve this problem, we propose a new multi-task method based on multivariate time series learning. Considering there are multiple product types and each type has different trend characteristics, we first cluster the time series of all product types and then model each as a multivariate prediction task. We then design a new dilated convolution network to fusion the features of related products and the trend characteristic of each task. Moreover, we develop a trend structural entropy network to grasp the fluctuation features of the task. A new self-enhancement mechanism is proposed to finely capture the correlations among tasks. Through multi-task learning, the model can effectively improve prediction accuracy by using the complementary information of closely related time series. Experimental results on two real data sets show the effectiveness of our approach. © 2022 SPIE.

关键词： Sales

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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MA2-FPN for Tiny Object Detection from Remote Sensing Images 15

MA2-FPN for Tiny Object Detection from Remote Sensing Images

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15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022

作者： Li, Saiwei Tong, Qiang Liu, Xuhong Cui, Zhanqi Liu, Xiulei College of Computer Beijing Information Science and Technology University Beijing China Beijing Information Science and Technology University Beijing Advanced Innovation Center for Materials Genome Engineering Laboratory of Data Science and Information Studies Beijing China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Tiny object detection has been a challenging topic in computer vision recent years. Moreover, in remote sensing field, smaller and clustered tiny objects make its detection more difficult compared to ground-based images. This makes general detectors fail to achieve good performance when facing tiny objects in remote sensing images. In this paper, we propose a Mask Augmented Attention Feature Pyramid Network(MA2-FPN) to detect tiny objects in remote sensing images, which consists of two modules, Attention Enhancement Module(AEM) and Mask Supervision Module(MSM). Specifically, AEM aggregates tiny target context and spatial feature information by large kernel separable convolutional attention mechanism, and MSM supervises AEM through a segmentation attention loss to aggregate attention information more accurately while suppressing the influence of irrelevant background. Experiments based on the AI-TOD benchmark show that our MA2-FPN achieves state-of-the-art(SOTA) level. © 2022 IEEE.

关键词： Object detection

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Advancing Multi-Party Dialogue Systems with Speaker-ware Contrastive Learning

arXiv

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arXiv 2025年

作者： Hu, Zhongtian Cui, Yiwen Li, Ronghan Zhao, Meng Wang, Lifang School of Computer Science and Engineering Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China School of Artificial Intelligence and Big Data Henan University of Technology China

Dialogue response generation has made significant progress, but most research has focused on dyadic dialogue. In contrast, multi-party dialogues involve more participants, each potentially discussing different topics, making the task more complex. Current methods often rely on graph neural networks to model dialogue context, which helps capture the structural dynamics of multi-party conversations. However, these methods are heavily dependent on intricate graph structures and dataset annotations, and they often overlook the distinct speaking styles of participants. To address these challenges, we propose CMR, a Contrastive learning-based Multi-party dialogue Response generation model. CMR uses self-supervised contrastive learning to better distinguish "who says what." Additionally, by comparing speakers within the same conversation, the model captures differences in speaking styles and thematic transitions. To the best of our knowledge, this is the first approach to apply contrastive learning in multi-party dialogue generation. Experimental results show that CMR significantly outperforms state-of-the-art models in multiparty dialogue response tasks. © 2025, CC BY.

关键词： Self-supervised learning

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BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning

arXiv

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arXiv 2024年

作者： Miao, Xiangyang Xiao, Guobao Wang, Shiping Yu, Jun School of Electronics and Information Engineering Tongji University China College of Computer and Data Science Fuzhou University China School of Computer Science and Technology Hangzhou Dianzi University China

Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasis on transitioning from local to global, resulting in the neglect of interactions between different contexts. To tackle this issue, we propose a parallel context learning strategy that involves acquiring bilateral consensus for the two-view correspondence pruning task. In our approach, we design a distinctive self-attention block to capture global context and parallel process it with the established local context learning module, which enables us to simultaneously capture both local and global consensuses. By combining these local and global consensuses, we derive the required bilateral consensus. We also design a recalibration block, reducing the influence of erroneous consensus information and enhancing the robustness of the model. The culmination of our efforts is the Bilateral Consensus Learning Network (BCLNet), which efficiently estimates camera pose and identifies inliers (true correspondences). Extensive experiments results demonstrate that our network not only surpasses state-of-the-art methods on benchmark datasets but also showcases robust generalization abilities across various feature extraction techniques. Noteworthily, BCLNet obtains 3.98% mAP5◦ gains over the second best method on unknown outdoor dataset, and obviously accelerates model training speed. The source code will be available at: https://***/guobaoxiao/BCLNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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