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Image retrieval algorithm based on response value center weighted convolution feature

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2022年第11期52卷 2669-2675页

作者： Li, Xiao-Ning Zhao, Hong-Wei Zhang, Dan-Yang Zhang, Yuan College of Computer Science and Technology Jilin University Changchun130012 China College of Computer Science and Technology Changchun Normal University Changchun130032 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

A novel cross-entropy-based loss is proposed，which is applied to a classical convolutional neural network to obtain a better embedding space. A convolutional feature aggregation algorithm based on the weighting of the center of the response value is designed to process the three-dimensional convolutional features obtained by the neural network. The algorithm obtains the spatial weighting coefficient of the feature map by calculating the current position response value and the Gaussian center. The algorithm reduces the loss of information when the 3D convolution feature map is reduced to a 1D image feature descriptor and realizes the enhancement of the target area. Finally，the obtained image feature descriptors are used for retrieval tasks. On the CUB-200-2011 dataset，the effectiveness of the loss function and the response value center weighting algorithm are respectively verified by ablation experiments. Compared with the current retrieval methods，higher precision and recall rates have been obtained on the four datasets of Paris6k，Oxford5k，CUB-200-2011 and CARS196. © 2022 Editorial Board of Jilin University. All rights reserved.

关键词： Convolution

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Tc-Sepm: Characterizing Soft Error Resilience of Cnns on Tensor Cores from Program and Microarchitecture Perspectives

SSRN

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SSRN 2023年

作者： Wei, Xiaohui Zhou, Changbao Yue, Hengshan Zhou, Joey Tianyi College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China Singapore138632 Singapore Singapore138632 Singapore

As an architectural CNN accelerator integrated into NVIDIA's GPUs, existing research mainly focus on improving the performance of Tensor Core. However, the highly integrated Tensor Cores are vulnerable to transient faults (i.e., soft errors), causing catastrophic consequences in safety-critical applications like automatic driving. Thus, it is imperative to estimate the reliability of CNNs on Tensor Cores. However, it is expensive to obtain a statistically significant resilience profile of CNNs on Tensor Cores with the existing fault injection(FI)-based reliability estimation methods. To this end, we build TC-SEPM to predict the error resilience of CNNs on Tensor Cores instead of FI methods. To ensure the accuracy of TC-SEPM, we first investigate resilience-related features from microarchitecture and program perspectives. Then, leveraging these heuristic features, we train machine learning models to learn the hidden relationship between error resilience and the investigated features, enabling us to predict the impact of soft errors in Tensor Cores on CNN output. Experimental results show that TC-SEPM achieves high accuracy for individual soft error resiliency prediction and overall program resilience estimation while its overhead is only 1/27 of FI methods. Additionally, TC-SEPM can provide valuable insights for programmers or architects to design more robust CNN models on Tensor Cores. © 2023, The Authors. All rights reserved.

关键词： Error correction

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2D-3D Feature Co-Embedding Network with Sparse Annotation for 3D Medical Image Segmentation

2D-3D Feature Co-Embedding Network with Sparse Annotation fo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhu Zhu Shuyu Guo Ziwei Wang Jianhang Jiao Tian Bai College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Software Jilin University Changchun China Department of Orthopedics The Second Norman Bethune Hospital of Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Supervised methods on 3D medical image segmentation need large amounts of annotated data, but annotating is time-consuming. Also, existing 3D segmentation methods capture more global structural information but overlook local detailed features, which negatively impacts the segmentation of small tissues. In this paper, we propose a novel weakly-supervised 2D-3D Feature Co-Embedding Network (2D-3D CoENet) that includes 2D and 3D encoding layers, simultaneously extracting 2D local detailed and 3D global structural features. To reduce annotation costs, we use fewer labeled slices as ground truth and pseudo-labels are generated by 2D-3D CoENet for other slices. Additionally, multi-view learning is introduced to capture more 2D local detailed information, and a Multi-view Semantic Consistency loss (MSC loss) is proposed to constrain features from multiple perspectives. To further enhance the local detailed texture features, we propose an Edge Enhancement Module (EEM) in the 3D segmentation network to enhance the edge detail features. Our experimental results on the SKI10 dataset and OAI ZIB dataset demonstrate that our method outperforms the SOTA weakly-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound results.

关键词： Image segmentation Three-dimensional displays Costs Upper bound Annotations Image edge detection Semantics Feature extraction Encoding Biomedical imaging

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Structure- and Logic-Aware Heterogeneous Graph Learning for Recommendation

Structure- and Logic-Aware Heterogeneous Graph Learning for ...

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International Conference on Data engineering

作者： Anchen Li Bo Yang Huan Huo Farookh Khadeer Hussain Guandong Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Aalto University Espoo Finland School of Computer Science University of Technology Sydney Sydney Australia Education University of Hong Kong Hong Kong China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure- and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets.

关键词： Geometry Semantics Contrastive learning Data engineering Boosting Cognition Graph neural networks

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A Multi-objective Optimization Approach for Secure Communications Based on Collaborative Beamforming in UAV Networks

A Multi-objective Optimization Approach for Secure Communica...

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IEEE Conference on Wireless Communications and Networking

作者： Xinrong Guo Fang Mei Geng Sun Tingting Zheng College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Electrical and Computer Engineering University of Waterloo Waterloo Canada

With the rapid development of wireless communication, unmanned aerial vehicle (UAV) networks have received extensive attention and been applied in many fields, but some challenges exist in their applications, such as the issue of security when implementing communication. In this paper, a virtual antenna array (VAA) is formed in multiple UAV units using collaborative beamforming (CB) technology. Under the interference of multiple eavesdroppers, secure communication with the ground base station (BS) is achieved. To achieve better performance, we formulate a multi-objective optimization problem for UAV network security communication (SCMOP), and jointly optimize the positions and excitation current weights of UAVs to set the null values in the direction of known eavesdroppers, reduce the sidelobe levels (SLLs) and enhance the directivity of the main lobe (ML). Since the formulated SCMOP is an NP-hard problem, we propose an improved the third non-dominated sorting genetic algorithm (IMNSGA-III) with chaos operator and crossover and mutation operators to solve the problem in this paper. The simulation results show that the IMNSGA-III can solve the SCMOP well and obtain the best results compared with other benchmark algorithms.

关键词： Chaos Wireless communication Array signal processing Null value Collaboration Autonomous aerial vehicles Statistics

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Learning Generalizable Agents via Saliency-Guided Features Decorrelation

arXiv

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arXiv 2023年

作者： Huang, Sili Sun, Yanchao Hu, Jifeng Guo, Siyuan Chen, Hechang Chang, Yi Sun, Lichao Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China School of Artificial Intelligence Jilin University China Department of Computer Science University of Maryland College Park United States Lehigh University BethlehemPA United States

In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background noise, and task-relevant features, such as robot configurations, that are related to the optimal decisions. To achieve generalization in both situations, agents are required to accurately understand the impact of changed features on the decisions, i.e., establishing the true associations between changed features and decisions in the policy model. However, due to the inherent correlations among features in the state space, the associations between features and decisions become entangled, making it difficult for the policy to distinguish them. To this end, we propose Saliency-Guided Features Decorrelation (SGFD) to eliminate these correlations through sample reweighting. Concretely, SGFD consists of two core techniques: Random Fourier Functions (RFF) and the saliency map. RFF is utilized to estimate the complex non-linear correlations in high-dimensional images, while the saliency map is designed to identify the changed features. Under the guidance of the saliency map, SGFD employs sample reweighting to minimize the estimated correlations related to changed features, thereby achieving decorrelation in visual RL tasks. Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Robustifying Vision Transformer for Image Forgery Localization with Multi-Exit Architectures

SSRN

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SSRN 2023年

作者： Shi, Zenan Chen, Haipeng Dong, Zhang College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

The proliferation of image manipulation tools has led to an increase in the number of manipulated images being disseminated online, posing risks like the propagation of fake news and telecom fraud. Thus, there is an increasing demand for precise, generic, and robust methods for detecting and locating manipulated images. In this paper, we propose a simple and clean model, named MEAFormer, for image forgery localization that does not heavily rely on pre-training models. MEAFormer comprises three main components: encoder network, neck network, and decoder network. Specifically, the transformer-based encoder network extracts hierarchical feature representations from the input image, providing rich contextual information in each layer. The neck network, incorporating our proposed cross-layer feature aggregation (CFA), aggregates these hierarchical features. To achieve better spatial feature co-occurrence, instead of using noise or edge artifacts, we introduce a multi-scale graph reasoning (MGR) module within the decoder network via bipartite graphs over the encoder and decoder features in a multi-scale fashion. The cross-level enhancement (CLE) further performs adjacent-level feature fusion to amplify the region-of-interests in aggregated manipulation features. Finally, the multi-exit architecture (MEA) guides the model to learn fine-grained features and segment out the manipulated region. Extensive experiments across diverse and challenging datasets conclusively establish MEAFormer’s superiority over existing state-of-the-art methods, excelling in accuracy, generalization, and robustness. © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Traffic jam prediction based on analysis of residents spatial activities

Traffic jam prediction based on analysis of residents spatia...

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2020 International Conference on Computer Information and Big Data Applications, CIBDA 2020

作者： Lv, Zhijin Fu, Hao Tang, Wei Chen, Xiaoxu College of Software Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China

ISBN: (纸本)9781728198378

The prediction of urban traffic congestion has always been one of the important contents in the research of intelligent transportation systems. The difficulty in predicting urban traffic congestion is that urban traffic operation is essentially a collection of spatial activity planning for residents under certain conditions. The huge group of residents themselves has great complexity and uncertainty. Traditional neural networks mostly focus on road characteristic data and road condition data, and lack of in-depth exploration of the fundamental factors of traffic congestion for residents' travel. So we propose a traffic congestion prediction model based on the analysis of residents' spatial activities. Starting from the residents' activities, the simulation of urban traffic operation conditions can more realistically reflect the traffic congestion situation of the city at specific times and roads, and quickly generate model results. The experimental results show that the model analyzed by residents' spatial activities runs fast and has high prediction accuracy. The results are in line with the actual situation and have strong practical value. © 2020 IEEE.

关键词： Traffic congestion

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Consistency label-activated region generating network for weakly supervised medical image segmentation

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Computers in Biology and Medicine 2024年 173卷 108380-108380页

作者： Du, Wei Huo, Yongkang Zhou, Rixin Sun, Yu Tang, Shiyi Zhao, Xuan Li, Ying Li, Gaoyang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East Hospital Shanghai200092 China Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai200092 China

The current methods of auto-segmenting medical images are limited due to insufficient and ambiguous pathonmorphological labeling. In clinical practice, rough classification labels (such as disease or normal) are more commonly used than precise segmentation masks. However, there is still much to be explored regarding utilizing these weak clinical labels to accurately determine the lesion mask and guide medical image segmentation. In this paper, we proposed a weakly supervised medical image segmentation model to directly generate the lesion mask through a class activation map (CAM) guided cycle-consistency label-activated region transferring network. Cycle-consistency enforces that the mappings between the two domains should be reversible, which ensures that the original image can be reconstructed from the translated image. We developed a complementary branches fusion module to address the issue of blurry boundaries in CAM-guided segmentation. The complementary branch preserves the original semantic information of the non-lesion region and perfectly fuses the transferred feature of the lesion region with a complementary mask-constrained fake image generation process to clear the boundary of the lesion and non-lesion regions. This module allows the class transformation to focus solely on the label-activated region, resulting in more explicit segmentation. This model can accurately identify different region of medical images at the pixel-level while preserving the overall semantic structure semantion. It organizes disease labels and corresponding regions during image synthesis. Our method utilizes a joint discrimination strategy that significantly enhances the precision of the produced lesion mask. Extensive experiments of the proposed method on BraTs, ISIC and COVID-19 datasets demonstrate superior performance over existing state-of-the-art methods. The code and datasets are available at: https://***/mlcb-jlu/MedImgSeg. © 2024 Elsevier Ltd

关键词： Image segmentation

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A Novel Local Search Algorithm Based on Connectivity Method and Vertex Penalty Mechanism for Minimum Weighted Connected Dominating Set Problem

SSRN

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SSRN 2024年

作者： Li, Ruizhi He, Jintao Yin, Minghao Hu, Shuli Ding, Nan Jilin University of Finance and Economics Changchun130117 China Northeast Normal University Changchun130117 China Business Big Data Research Center of Jilin Province Changchun130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

The minimum weighted connected dominating set problem is a classical NP-hard problem with wide applications in practice. In this paper, an efficient local search algorithm is proposed to solve this problem. In this algorithm, firstly, an initial solution construction method based on greedy randomized adaptive strategy is adopted, which can balance the quality and the diversity of the initial solutions. Second, two new scoring functions, Ascore and Cscore, are proposed to increase the diversity of deleted vertices. Third, a new connectivity method is proposed so that the algorithm no longer relies on the shortest path between two connected components. Fourth, a vertex penalty mechanism is introduced, which helps the algorithm to jump out of the local optimum effectively. Fifth, two scoring functions, Ascore and Cscore, are combined with the vertex penalty mechanism to propose an effective vertex selection method, which helps the algorithm to select suitable vertices to be added to or removed from the current candidate solution. Finally, the propsoed local search algorithm is compared with seven state-of-the-art algorithms on three types of benchmark instances. The experimental results show that the algorithm exhibits better performance on these three types of classical benchmark instances. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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