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Uncertainty-aware coarse-to-fine alignment for text-image person retrieval

Visual Intelligence 2025年第1期3卷 1-14页

作者： Deng, Yifei Chen, Zhengyu Li, Chenglong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei 230601 China

Text-to-image person retrieval, a fine-grained cross-modal retrieval problem, aims to search for person images from an image library that match a given textual caption. Existing text-to-image person retrieval methods usually use fixed-point embedding to express the semantics of the two modalities and perform multi-granularity alignment between modalities in the embedding space. However, owing to the inherent mutual one-to-many correspondence between images and texts, it is often difficult for fixed-point embedding methods to adequately capture this relationship, leading to erroneous retrieval results. To address this problem, we propose a novel uncertainty-aware coarse-to-fine alignment method, which first maps fixed-point embedding to probability distributions and then aligns two modalities in terms of distributions and sampling points at a coarse-to-fine granularity, for accurate text-to-image person retrieval. Specifically, we first introduce two contrastive learning tasks of distribution contrast learning and point contrast learning, to achieve coarse-grained inter-modal alignment with uncertainty-aware. The distribution contrast learning task ensures that distributions with the same identity are as similar as possible across modalities through distribution-based contrastive learning. The point contrast learning task performs the contrastive learning of inter-modal and intra-modal sampling points, which not only models rich and diverse cross-modal associations, but also optimizes the learning of distributions. For the fine-grained association requirements of text-to-image person retrieval, we design the task of uncertainty-aware attribute masking language reconstruction, which achieves fine-grained alignment by randomly masking attribute words in the text and reconstructing them via inter-modal sample point interactions. Extensive experiments on two public datasets demonstrate the superior performance of our method. © The Author(s) 2025.

关键词： Coarse-to-fine alignment Contrastive learning Cross-modal retrieval Probalility distribution Uncertainty-aware

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Research on mixed decision implications based on formal concept analysis

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International Journal of Cognitive Computing in Engineering 2023年第1期4卷 71-77页

作者： Ren, Xingguo Li, Deyu Zhai, Yanhui School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China

Decision implication is an important form of knowledge representation and acquisition in Formal Concept Analysis. Decision implication reduces the redundancy of knowledge extracted from data. However, decision implication cannot extract negative information from data, so there is information loss in decision implication. This paper introduces negative attributes to decision implication and proposes mixed decision implication, enabling decision implication to extract negative knowledge from data and to represent richer decision knowledge. This paper studies the logical systems of mixed decision implications. The semantical system of mixed decision implications is constructed to represent and deduce sound mixed decision implications and avoid contradictory mixed decision implications. In the syntactical system, Mixed Augmentation and Mixed Combination are introduced and the soundness, completeness and non-redundancy of these two inference rules are proved. © 2023

关键词： Formal concept analysis

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A comprehensive survey of visible infrared person re-identification from an application perspective

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Multimedia Tools and Applications 2024年第42期83卷 90243-90270页

作者： Chang, Hua Xu, Xin Liu, Wei Lu, Lingyi Li, Weigang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

Person re-identification (ReID) is a significant issue in computer vision, aiming to match the same pedestrian across various cameras. Recent research on this issue has successfully reached a satisfactory performance level in the daytime scenario. However, the performance of ReID in low illumination scenarios suffers a dramatic degradation due to the insufficient personal information captured by visible cameras in low or no light scenarios. To address this challenge, the adoption of novel camera technologies for more effective pedestrian data is imperative. Owing to the constraints of distance and usage scenarios in deploying depth cameras, practical infrared cameras are frequently utilized, spurring research in visible infrared person re-identification (VI-ReID). Significant progress has been made in the field of VI-ReID in recent years, but only a few reviews have categorized this problem based on methodology. This article offers a novel perspective by classifying VI-ReID according to application scenarios. Following our categorization scheme, the main advancements in deep learning for VI-ReID over the past five years have been grouped into three categories as follows: retrieval of infrared images using visible images (V→IR), retrieval of visible images using infrared images (IR→V), and joint retrieval of infrared and visible images (V→IR and IR→V). Next, this survey outlines the available datasets and evaluation metrics. After that, we categorize diverse methodologies under the proposed classification and assess their performance metrics across common datasets. Finally, we present two prospective avenues for future research in this domain, informed by the current state of advancement. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Facile intercalation of alkali ions in WO_(3)for modulated electronic and optical properties:Implications for artificial synapses and chromogenic application

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Science China(Physics,Mechanics & Astronomy) 2024年第2期67卷 149-160页

作者： Changmeng Huan Zihan Lu Silin Tang Yongqing Cai Qingqing Ke School of Microelectronics Science and Technology Sun Yat-sen UniversityZhuhai 519082China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen UniversityZhuhai 519082China Institute of Applied Physics and Materials Engineering University of MacaoTaipaMacao 999078China

Tungsten oxides(WO_(3))are widely recognized as multifunctional systems owing to the existence of rich *** diverse phases exhibit distinct octahedra-tilting patterns,generating substantial tunnels that are ideally suited for ***,a quantitative comprehension regarding the impact of distinct phases on the kinetics of intercalated conducting ions remains ***,we employ first-principles calculations to explore the spatial and orientational correlations of ion transport inγ-and h-WO_(3),shedding light on the relationship between diffusion barriers and the size of the conducting *** findings reveal that different types and concentrations of alkali-metals induce distinct and continuous lattice distortions in WO_(3)***,γ-WO_(3)is more appropriate to accommodate Li+ions,exhibiting a diffusion barrier and coefficient of 0.25 eV and 9.31×10^(-8)cm^(2)s^(-1),***,h-WO_(3)features unidirectional and sizeable tunnels that facilitate the transport of K+ions with an even lower barrier and a high coefficient of 0.11 e V and 2.12×10^(-5)cm^(2)s^(-1),***,the introduction of alkali-metal into WO_(3)tunnels tends to introduce n-type conductivity by contributing s-electrons to the unoccupied W 5d states,resulting in enhanced conductivity and tunable electronic *** alkali metals in WO_(3)tunnels are prone to charge transfer,forming small polaronic states and modulating the light absorption in the visible and nearinfrared *** tunable electronic and optical properties,combined with the high diffusion coefficient,underscore the potential of WO_(3)in applications such as artificial synapses and chromogenic devices.

关键词： WO_(3)polymorphs ion insertion kinetic activity tunable electronic and optical properties iontronics

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ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection

ResPocket: A Multi-Scale Feature Fusion Method for Improving...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Yang, Wenli Chen, Yaoyu Liao, Xiongwei Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology China

ISBN: (纸本)9798350386226

Accurate detection of protein binding sites is critical for facilitating drug design. Most existing models rely on features at a single scale, leading to the loss of crucial global or local structural information. Therefore, this paper proposes a novel 3D model (ResPocket), to fully capture the multi-scale features of proteins. First, Residual Block (ResB) based on U-Net is used to capture features from different scales, including global and local structural information. Then, an Enhanced Attention Module (EAM) is designed to encode semantic dependencies by effectively capturing global context information. In addition, ResPocket focuses on learning local information about protein pockets with Self-Guided Mask Block (SGMB), to increase the robustness of the overall model and reduce its dependence on external mask guidance. Experimental results show that ResPocket improves by an average of 4.89% on DCA Top-n, by an average of 1.48% and 2.25% on DCC and DVO metrics, respectively. © 2024 IEEE.

关键词： Binding sites

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A New Method of Image Restoration technology Based on WGAN

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computer Systems Science & Engineering 2022年第5期41卷 689-698页

作者： Wei Fang Enming Gu Weinan Yi Weiqing Wang Victor S.Sheng School of Computer&Software Engineering Research Center of Digital ForensicsMininstry of EducationNanjing University of Information Science&TechnologyNanjing210044China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou215325China Texas Tech University USA

With the development of image restoration technology based on deep learning,more complex problems are being solved,especially in image semantic inpainting based on ***,image semantic inpainting techniques are becoming more ***,due to the limitations of memory,the instability of training,and the lack of sample diversity,the results of image restoration are still encountering difficult problems,such as repairing the content of glitches which cannot be well integrated with the original ***,we propose an image inpainting network based on Wasserstein generative adversarial network(WGAN)*** the corresponding technology having been adjusted and improved,we attempted to use the Adam algorithm to replace the traditional stochastic gradient descent,and another algorithm to optimize the training used in recent *** evaluated our algorithm on the ImageNet *** obtained high-quality restoration results,indicating that our algorithm improves the clarity and consistency of the image.

关键词： Image restoration WGAN DCGAN context semantic

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Data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Mitigating Shortcut Learning via Smart Data Augmentation based on Large Language Model 31

Mitigating Shortcut Learning via Smart Data Augmentation bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sun, Xinyi Tan, Hongye Guo, Yaxin Qiang, Pengpeng Li, Ru Zhang, Hu School of Computer and Information Technology Shanxi University Taiyuan China Institute of Intelligent Information Processing Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China

ISBN: (纸本)9798891761964

Data-driven pre-trained language models typically perform shortcut learning wherein they rely on the spurious correlations between the data and the ground truth. This reliance can undermine the robustness and generalization of the model. To address this issue, data augmentation emerges as a promising solution. By integrating anti-shortcut data to the training set, the models' shortcut-induced biases can be mitigated. However, existing methods encounter three challenges: (1) Manual definition of shortcuts is tailored to particular datasets, restricting generalization. (2) The inherent confirmation bias during model training hampers the effectiveness of data augmentation. (3) Insufficient exploration of the relationship between the model performance and the augmented data quantity may result in excessive data consumption. To tackle these challenges, we propose a method of Smart Data Augmentation based on Large Language Models (SAug-LLM). It leverages the LLMs to autonomously identify shortcuts and generate their anti-shortcut counterparts. In addition, the dual validation is employed to mitigate the confirmation bias during the model retraining. Furthermore, the data augmentation process is optimized to effectively rectify model biases while minimizing data consumption. We validate the effectiveness and generalization of our method through extensive experiments across various natural language processing tasks, demonstrating an average performance improvement of 5.61%. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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Research on improved algorithm of FP-growth based on data strong association

Research on improved algorithm of FP-growth based on data st...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Ye, Qianhao Ning, Shiyong School of Computer and Information Engineering Harbin University of Commerce Heilongjiang Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing China

ISBN: (纸本)9781510671881

FP-Growth algorithm is a classic algorithm in the field of data mining. Compared with Apriori algorithm, all frequent item sets can be obtained by traversing the data set only twice, which improves the efficiency of data mining. However, in practical use, if there are many Frequent one item sets in the data set, the data set is scattered, and a lot of time will be wasted. Moreover, it will cause the problem that there are too many child nodes when FP-Tree is formed, and it is easy to overflow. In view of the above shortcomings, in this paper, a new FP-Growth algorithm based on Data Strong Association (SDA-FP-Growth algorithm) is proposed., which uses frequent two items set for operation integration and merging of the same leaf nodes, which is conducive to the subsequent frequent item set search. Experiments show that the SDA-FP-Growth algorithm reduces the running time and memory space. © 2023 SPIE. All rights reserved.

关键词： Data mining

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U-CCNet: Brain Tumor MRI Image Segmentation Model with Broader Global Context Semantic information Abstraction 7

U-CCNet: Brain Tumor MRI Image Segmentation Model with Broad...

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7th IEEE Advanced information technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Han, Xiaowei Liu, Jiwu Zhao, Jiayuan Harbin University of Commerce School of Computer and Information Engineering Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China Flow Station of Applied Economics Harbin China

ISBN: (纸本)9798350339161

Since brain tumor detection can help in the diagnosis of diseases such as Alzheimer's disease, segmentation and classification of brain tumors are crucial for diagnosis and treatment. Although radiologists can find the tumor area using magnetic resonance imaging (MRI), it is more time-consuming. The high-precision segmentation results provided by deep learning models can assist radiologists in better-identifying brain tumors. In this paper, we propose a U-Net-based approach to detect brain tumors in MRI. In this paper, the cross-attention mechanism is added to the U-Net benchmark architecture and trained and tested on the LGG Segmentation dataset (TCGA-LGG) dataset. The test results show that the proposed model performs well in brain tumor MRI segmentation tasks. Finally, the proposed U-CCNet model is compared with other studies. Experimental results show that this study is superior to other models in the Dice similarity coefficient and IoU index. The proposed method is tested on the LGG brain MRI segmentation dataset. There is a significant improvement in segmentation accuracy in terms of dice score and mIoU, reducing the use of computationally expensive 3D networks. © 2024 IEEE.

关键词： Magnetic resonance imaging

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