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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Li, Changxing Chen, Zhuang Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing China Chongqing University of Technology School of Computer Science and Engineering Chongqing China

ISBN: (纸本)9798350331417

In view of the insufficient ability of the currently existing deep learning-based methods to repair image high-frequency information and the small sensory field of the traditional convolutional methods. A two-stage image repair model is proposed. First, Transformer is used to repair the image initially;second, Fourier convolution is used to construct a generative network to refine the high-frequency details of the image;finally, the repair results are input into the discriminator for training to obtain the final image repair model. The experimental results on street view datasets show that the model in the paper has better restoration results in high-frequency details as well as semantic information;compared with mainstream methods, It achieves better results in peak signal-to-noise ratio and structural similarity. © 2023 IEEE.

关键词： Generative adversarial network image inpainting multiple attention Receptive field

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RGA-Reid: Neural Architecture Search Based on Relation-Aware Global Attention for Person Re-Identification 11

RGA-Reid: Neural Architecture Search Based on Relation-Aware...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Chen, Shijie Chen, Shengbo Lei, Zhou School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9798350333664

With the continuous development of deep learning, reid has been continually improved. However, the network backbones of reid are mostly manually designed. This paper proposes RGA-Reid based on auto-reid. NASBR-Reid obtains the network backbone through neural architecture search. It replaces the part-base module in the auto-reid search algorithm with the RGA module, which effectively integrates global relationships to enhance attention training between different parts. Experimental results demonstrate that RGA-Reid can retrieve superior network backbones. © 2023 IEEE.

关键词： network backbone Neural Architecture Search Reid Relation-Aware Global Attention

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Enhancing Chest X-ray Diagnostics with Neighbor-assisted Multimodal Integration

Enhancing Chest X-ray Diagnostics with Neighbor-assisted Mul...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xu, Chenjie Pan, Yang Hu, Bing Zhang, Yang Hong, Yi Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Shanghai Baosight Software Co. Ltd. Shanghai China

ISBN: (纸本)9798350386226

In the field of medical imaging analysis, particularly in interpreting chest X-rays, deep learning models have shown remarkable progress. Nonetheless, these models often face challenges such as limited annotation and inadequate utilization of public data resources. This is particularly apparent with databases containing multimodal data, such as images and medical reports, where the effective integration of this multimodal information remains difficult. To address these limitations, we propose the Neighbor-Assisted Multimodal Attention Network (NAMAN), a novel approach designed to leverage retrieval augmentation techniques to enhance disease classification performance. NAMAN combines nearest neighbor search with multimodal fusion, utilizing both visual features from similar X-ray images and textual information from corresponding medical records. The experimental results demonstrate the efficacy of incorporating retrieved neighbor information and multimodal integration mechanisms in NAMAN. Our ablation studies offer insights into the optimal configuration of the model, including the effects of various attention mechanisms and the number of retrieved neighbors. This work contributes to the expanding field of retrieval-augmented approaches in medical imaging, presenting a promising avenue for leveraging large-scale, multimodal medical databases to enhance diagnostic accuracy and reliability. © 2024 IEEE.

关键词： Integration

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Phase Re-Service in Reinforcement Learning Traffic Signal Control 27

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zhang, Zhiyao Gunter, George Quinones-Grueiro, Marcos Zhang, Yuhang Barbour, William Biswas, Gautam Work, Daniel Institute for Software Integrated Systems Vanderbilt University Department of Civil and Environmental Engineering United States Institute for Software Integrated Systems Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798331505929

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation. © 2024 IEEE.

关键词： Markov processes

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Unsupervised feature selection via multiple graph fusion and feature weight learning

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science China(Information sciences) 2023年第5期66卷 56-72页

作者： Chang TANG Xiao ZHENG Wei ZHANG Xinwang LIU Xinzhong ZHU En ZHU School of Computer Science China University of Geosciences State Key Laboratory for Novel Software Technology Nanjing University School of Computer National University of Defense Technology National Supercomputing Center in Jinan Qilu University of Technology (Shandong Academy of Sciences) College of Mathematics Physics and Information Engineering Zhejiang Normal University

Unsupervised feature selection attempts to select a small number of discriminative features from original high-dimensional data and preserve the intrinsic data structure without using data labels. As an unsupervised learning task, most previous methods often use a coefficient matrix for feature reconstruction or feature projection, and a certain similarity graph is widely utilized to regularize the intrinsic structure preservation of original data in a new feature space. However, a similarity graph with poor quality could inevitably afect the final results. In addition, designing a rational and efective feature reconstruction/projection model is not easy. In this paper, we introduce a novel and efective unsupervised feature selection method via multiple graph fusion and feature weight learning（MGF2WL） to address these issues. Instead of learning the feature coefficient matrix, we directly learn the weights of diferent feature dimensions by introducing a feature weight matrix, and the weighted features are projected into the label space. Aiming to exploit sufficient relation of data samples, we develop a graph fusion term to fuse multiple predefined similarity graphs for learning a unified similarity graph, which is then deployed to regularize the local data structure of original data in a projected label space. Finally, we design a block coordinate descent algorithm with a convergence guarantee to solve the resulting optimization problem. Extensive experiments with sufficient analyses on various datasets are conducted to validate the efficacy of our proposed MGF2WL.

关键词： unsupervised feature selection graph fusion feature weight learning local structure preservation

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Few-shot Semantic Segmentation via Perceptual Attention and Spatial Control 24

Few-shot Semantic Segmentation via Perceptual Attention and ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Shi, Guangchen Zhu, Wei Wu, Yirui Zhao, Danhuai Zheng, Kang Lu, Tong National Key Lab for Novel Software Technology Nanjing University Nanjing China China Mobile Zijin Innovation Insititute Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798400706868

Few-shot semantic segmentation (FSS) aims to locate pixels of unseen classes with clues from a few labeled samples. Recently, thanks to profound prior knowledge, diffusion models have been expanded to achieve FSS tasks. However, due to probabilistic noising and denoising processes, it is difficult for them to maintain spatial relationships between inputs and outputs, leading to inaccurate segmentation masks. To address this issue, we propose a Diffusion-based Segmentation network (DiffSeg), which decouples probabilistic denoising and segmentation processes. Specifically, DiffSeg leverages attention maps extracted from a pretrained diffusion model as support-query interaction information to guide segmentation, which mitigates the impact of probabilistic processes while benefiting from rich prior knowledge of diffusion models. In the segmentation stage, we present a Perceptual Attention Module (PAM), where two cross-attention mechanisms capture semantic information of support-query interaction and spatial information produced by the pretrained diffusion model. Furthermore, a self-attention mechanism within PAM ensures a balanced dependence for segmentation, thus preventing inconsistencies between the aforementioned semantic and spatial information. Additionally, considering the uncertainty inherent in the generation process of diffusion models, we equip DiffSeg with a Spatial Control Module (SCM), which models spatial structural information of query images to control boundaries of attention maps, thus aligning the spatial location between knowledge representation and query images. Experiments on PASCAL-5i and COCO datasets show that DiffSeg achieves new state-of-the-art performance with remarkable advantages. © 2024 ACM.

关键词： Semantic Segmentation

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Data Hybrid Attacks Based on Sensitive Users in Recommender Systems 7

Data Hybrid Attacks Based on Sensitive Users in Recommender ...

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Zhou, Hongwei Gao, Min Chao, Meiling Song, Yuqi Xiong, Qingyu Wen, Junhao Zhang, Yi Chongqing University Big Data And Software Engineering Chongqing China University of South Carolina Computer Science And Engineering ColumbiaSC United States

ISBN: (纸本)9798350359145

Recommender systems, which make precise recommendations through historical interaction data of users and items, have been widely used in real life. But at the same time, because of the open nature of the systems, they are vulnerable to malicious attacks. To explore the robustness of recommender systems, researchers have proposed various attack methods and analyzed their adverse effects. Although the current attack methods can make good results on rating-based recommender systems, they lack thinking about an attack for a social recommendation. Social relationships have a non-negligible role in recommender systems, but there is still little related research, which only constructs fake rating profiles and fake relationship profiles with simple rules, without considering the mutual influence of rating and social information. To address the above problem, we study the attack method with deeply hidden features and propose a hybrid attack method based on sensitive users. It can further enhance the hidden features of fake users, and then use social attacks to assist in attacking social recommendation methods. Empirical results on three public datasets show that our method outperforms traditional methods in terms of attack effectiveness and has excellent anti-detection capability. Ultimately, we hope that our method could enable more analysis for such a hybrid attack and guides for investigating effective prevention measures. © 2023 IEEE.

关键词： Recommender systems

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Considerations in Prioritizing for Efficiently Refactoring the Data Clumps Model Smell: A Preliminary Study 19

Considerations in Prioritizing for Efficiently Refactoring t...

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19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Baumgartner, Nils Iyenghar, Padma Pulvermüller, Elke Software Engineering Research Group School of Mathematics/Computer Science/Physics Osnabrück University Osnabrück49090 Germany innotec GmbH Hornbergstrasse 45 Filderstadt70794 Germany Faculty of Engineering and Computer Science HS Osnabrueck Osnabrück49009 Germany

ISBN: (纸本)9789897586965

This paper delves into the importance of addressing the data clumps model smell, emphasizing the need for prioritizing them before refactoring. Qualitative and quantitative criteria for identifying data clumps are outlined, accompanied by a systematic, simple but effective approach involving a weighted attribute system with threshold-based priority assignment. The paper concludes with an experimental evaluation of the proposed method, offering insights into critical areas for developers and contributing to improved code maintenance practices and overall quality. The approach presented provides a practical guide for enhancing software system quality and sustainability. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： software design

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Enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering 6th

Enhancing Weakly Supervised Medical Segmentation via Hetero...

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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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Accurate Anomaly Detection in Medical Images using Transfer Learning and Data Optimization: MRI and CT as Case Studies 5

Accurate Anomaly Detection in Medical Images using Transfer ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Nizamli, Yasser Filatov, Anton Yu. Fadel, Weaam Shichkina, Yulia A. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science Saint Petersburg Russia

ISBN: (纸本)9798350363739

Today, medical imaging techniques are widely used to detect a variety of human conditions and diseases. To speed up the diagnostic process, systems are often automated using deep learning methods, which have been proven to yield outstanding results. However, these models may be limited to a specific domain, prone to overfitting, and difficult to update. In this paper, we present a transfer learning approach that achieves high accuracy for detecting anomalies in both MRI and CT images. Our system utilizes the ConvNeXt network, inspired by the Swin Transformer architecture, as a high-level feature extractor from processed and augmented medical images. The resulting data is then optimized using the Edited Nearest Neighbors algorithm, which selects the most effective observations and discards the rest. Finally, the data is passed to the K-Nearest Neighbors classifier to detect anomalies in numerically represented images. Our model achieves high performance for two different types of medical images and can easily be updated without the need for retraining. © 2024 IEEE.

关键词： Magnetic resonance imaging

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