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42nd Chinese Control Conference, CCC 2023

作者： Yang, Chen Guo, Jian Zhang, Yibo Huang, Yanan Guo, Zijie School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China

ISBN: (纸本)9789887581543

Finger vein image quality assessment is used to evaluate the quality of images, namely to evaluate the applicability of the finger vein images to the recognition system. The work of quality assessment will largely affects the performance of the finger vein recognition system. In previous work, people usually design quality indicators manually, construct quality functions or perform supervised learning based on visual inspection, which has limitations of subjectivity. In order to solve these problems, this paper proposes a finger vein image quality assessment algorithm based on Stochastic Embedding Robustness (SER), which is called SER algorithm in this paper. The algorithm generates random subnetworks from the deployed finger vein recognition system, and indirectly evaluates the image quality by calculating the variation of different subnetworks. The simulation results show that, SER algorithm can effectively filter out low-quality images and improve the performance of the recognition system. Compared with other methods, it is more efficient and stable. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Image quality

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Verifier-local revocation group signatures with backward unlinkability from lattices

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Frontiers of Information technology & Electronic Engineering 2022年第6期23卷 876-892页

作者： Yanhua ZHANG Ximeng LIU Yupu HU Yong GAN Huiwen JIA College of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhou 450001China College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Stale Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China College of Information Engineering Zhengzhou University of TechnologyZhengzhou 450044China School of Mathematics and Information Science Guangzhou UniversityGuangzhou 510006China

For group signature(GS)supporting membership revocation,verifier-local revocation(VLR)mechanism seems to be a more flexible choice,because it requires only that verifiers download up-to-date revocation information for signature verification,and the signers are not *** a post-quantum secure cryptographic counterpart of classical number-theoretic cryptographic constructions,the first lattice-based VLR group signature(VLR-GS)was introduced by Langlois et al.(2014).However,none of the contemporary lattice-based VLR-GS schemes provide backward unlinkability(BU),which is an important property to ensure that previously issued signatures remain anonymous and unlinkable even after the corresponding signer(i.e.,member)is *** this study,we introduce the first lattice-based VLR-GS scheme with BU security(VLR-GS-BU),and thus resolve a prominent open problem posed by previous *** new scheme enjoys an O(log N)factor saving for bit-sizes of the group public-key(GPK)and the member’s signing secret-key,and it is free of any public-key *** the random oracle model,our scheme is proven secure under two well-known hardness assumptions of the short integer solution(SIS)problem and learning with errors(LWE)problem.

关键词： Group signature Lattice-based cryptography Verifier-local revocation Backward unlikability Short integer solution

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AlignFuse: Multi-feature Alignment and Fusion for Medical Image Segmentation 8

AlignFuse: Multi-feature Alignment and Fusion for Medical Im...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Chao, Shengyang Liu, Zijun Huang, Lili Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

ISBN: (纸本)9798350302455

U-Net, which is a U-shaped network, has made great progress on numerous medical image segmentation tracks. However, even if convolutional neural network (CNN) has a talent for extracting local information, it falls short in capturing global information due to the inherent drawback of the convolution operation. In contrast, whereby the design of tokenized images and self-attention mechanism, Transformer has a brilliant performance in modeling global contextual information but has a poor performance in grasping low-level details on account of the patch division which leads to ignorance of local correlation in each patch. In this paper, we develop a hybrid network structure, AlignFuse, which is in possession of the advantages of both CNN and Transformer. AlignFuse fits CNN and Transformer together in a parallel style, where Transformer leverages the high-level features and CNN pays more attention to details. Moreover, owing to the downsampling operation in CNNs, traditional context-free feature aggregation operations lead to misalignment issues between feature maps. To alleviate this problem, we propose to use Feature Aligned Fusion Module (FAFM) to effectively align and fuse the feature maps from CNN and Transformer in each decoder stage. We conduct experiments on Gland, breast cancer, and automatic cardiac segmentation, with competitive results obtained. © 2023 IEEE.

关键词： Convolution

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VRLVMix: Combating Noisy Labels with Sample Selection based on Loss Variation 4

VRLVMix: Combating Noisy Labels with Sample Selection based ...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Wang, Hailun Tu, Zhengzheng Jiang, Bo Ding, Yuhe Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center AHU-IAI Ai Joint Laboratory Hefei China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350343618

Since deep neural networks can fully fit all data, including noisy labels, that is, mislabeled data, this will be detrimental to the robustness and generalization ability of the network. To address this problem, existing methods usually use small loss tricks to select clean samples for training. However, sample selection using only small loss techniques cannot distinguish between large loss 'hard' samples and noise samples. In this work, we analyze the reasons why clean samples are misidentified as noise samples and propose a balanced selection mechanism based on loss variation. This method sorts the amount of loss change, uses variance to count the rankings of multiple epochs to describe the stability of loss variation, and selects samples with stable loss as clean samples to reduce the misclassification of 'hard' samples. At the same time, semantic clustering is used to assist resampling and reweighting, thereby alleviating the negative impact 1 of class imbalance on sample selection. We conduct comparative experiments and ablation experiments on synthetic noise datasets and real-world datasets such as CIFAR-10/100 and Clothing1M. Our VRLVMix has been shown to outperform numerous state-of-the-art methods, as evidenced by the experimental results. Moreover, it demonstrates the ability to extract clean samples even from large loss sample. © 2023 IEEE.

关键词： Semantics

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Attention Feature Selection Based on Prior Knowledge Injection 8

Attention Feature Selection Based on Prior Knowledge Injecti...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhao, Haifeng Chen, Quanman Huang, Lili Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

ISBN: (纸本)9798350302455

High-dimensional data generally appear in various analysis tasks, but high-dimensional data often causes computational waste and noise interference. Feature selection can make the model achieve better performance by preserving the optimal feature subset in the data to solve the problem of data dimensionality reduction. It is a popular method to select feature subset by combining autoencoder structure reconstruction. However, most autoencoder methods only take reconstruction as the objective function, which will lead to a decrease in the ability of feature discrimination for data with obvious clustering structure. Moreover, only decoder is used as an auxiliary structure, and the performance of the decode cannot be further released, resulting in the sub-optimal feature subset obtained. In this paper, we obtain the internal clustering matrix of the data in advance through manifold learning and spectral analysis, which we call prior knowledge. By constraining the distance between the decoder output and the prior knowledge, the model can force learning the clustering information of the data and improve the ability to identify the feature. Secondly, an attention module is designed to highlight the feature information of key locations. We prove the effectiveness of our method by experiments on several standard datasets. © 2023 IEEE.

关键词： Spectrum analysis

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Spatial-Spectral Transformer for Local and Global Hyperspectral Image Classification

Spatial-Spectral Transformer for Local and Global Hyperspect...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： MengXin Cao Guixin Zhao Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep learning techniques have become increasingly prevalent in hyperspectral image (HSI) classification. However, convolutional neural network (CNN)-based approaches often struggle with inefficient utilization of spectral sequence data and inadequate processing of fine-grained spectral details. In contrast, transformer models are inherently designed to capture sequence dependencies across spectral bands. Yet, most existing approaches tend to focus on either local or global feature extraction separately, which limits their ability to fully exploit the spectral information. To overcome these limitations, we propose the Local and Global Fusion Spectral Transformer (LGFST), an innovative architecture that combines Swin Transformer blocks, standard Transformer blocks, and a novel Local-Global Fusion (LGF) module. This architecture is designed to simultaneously capture both local and global features, thereby improving the extraction of detailed spectral information. The Swin Transformer block, integrated with patch merging and hierarchical attention mechanisms, expands the receptive field and enhances local feature representation through both intra- and cross-window interactions. Meanwhile, the Transformer block performs self-attention across all tokens to extract global features. The fusion of local and global features from these two blocks effectively improves the texture and discriminative power of spectral data, leading to enhanced classification performance, especially in scenarios with dense class distributions. Finally, the LGF module facilitates interaction between local and global feature sets, ensuring a seamless integration of information from both blocks. Extensive experiments on three benchmark HSI datasets demonstrate that LGFST outperforms conventional transformer models and sets new benchmarks in performance compared to state-of-the-art methods.

关键词： Merging Benchmark testing Transformers Feature extraction Distortion Sensors Data mining Standards Hyperspectral imaging Image classification

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Transformer-Based Deep Hashing Method for Multi-Scale Feature Fusion 48

Transformer-Based Deep Hashing Method for Multi-Scale Featur...

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

作者： He, Chao Wei, Hongxi Inner Mongolia University School of Computer Science Hohhot China Provincial Key Laboratory of Mongolian Information Processing Technology Hohhot China National and Local Joint Engineering Research Center of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9781728163277

The deep image hashing aims to map the input image into simply binary hash codes via deep neural networks. Motivated by the recent advancements of Vision Transformers (ViT), many deep hashing methods based on ViT have been proposed. Nevertheless, the ViT has enormous number of model parameters and high computational complexity. Moreover, the last layer of the ViT outputs only the classification tokens as image feature vectors, while the rest of the vectors are discarded. This results in the inefficiency of the model computation and the neglect of useful image information. Therefore, this paper proposes a Transformer-based deep hashing method for multi-scale feature fusion (TDH). Specifically, we use a hierarchical Transformer backbone to capture both global and local features of images. The hierarchical Transformer utilizes a local self-attention mechanism to process image blocks in parallel, which reduces computational complexity and promotes computational efficiency. Multi-scale feature fusion module captures all image feature vectors of the hierarchical Transformer output to obtain more enriched image feature information. We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the proposed method in this paper indicates superior results compared to the existing state-of-the-art work. Source code is available https://***/shuaichaochao/TDH. © 2023 IEEE.

关键词： deep hashing hash code image retrieval multi-scale feature fusion Transformer

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Domain Adaptive Lung Nodule Detection in X-ray Image

arXiv

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arXiv 2024年

作者： Zhao, Haifeng Jiang, Lixiang Ma, Leilei Sun, Dengdi Fu, Yanping The Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Medical images from different healthcare centers exhibit varied data distributions, posing significant challenges for adapting lung nodule detection due to the domain shift between training and application phases. Traditional unsupervised domain adaptive detection methods often struggle with this shift, leading to suboptimal outcomes. To overcome these challenges, we introduce a novel domain adaptive approach for lung nodule detection that leverages mean teacher self-training and contrastive learning. First, we propose a hierarchical contrastive learning strategy to refine nodule representations and enhance the distinction between nodules and background. Second, we introduce a nodule-level domain-invariant feature learning (NDL) module to capture domain-invariant features through adversarial learning across different domains. Additionally, we propose a new annotated dataset of X-ray images to aid in advancing lung nodule detection research. Extensive experiments conducted on multiple X-ray datasets demonstrate the efficacy of our approach in mitigating domain shift impacts. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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E-GPS: Explainable Geometry Problem Solving via Top-Down Solver and Bottom-Up Generator

E-GPS: Explainable Geometry Problem Solving via Top-Down Sol...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wenjun Wu Lingling Zhang Jun Liu Xi Tang Yaxian Wang Shaowei Wang Qianying Wang School of Computer Science and Technology Xi’ an Jiaotong University China Key Laboratory of Intelligent Networks and Network Security Ministry of Education Xi’an Shaanxi China Lenovo Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Geometry Problem Solving has drawn growing attention recently due to its application prospects in intelligent ed-ucation field. However, existing methods are still inade-quate to meet the needs of practical application, suffering from the following limitations: 1) explainability is not en-sured which is essential in real teaching scenarios; 2) the small scale and incomplete annotation of existing datasets make it hard for model to comprehend geometric knowl-edge. To tackle the above problems, we propose a novel method called Explainable Geometry Problem Solving (E-GPS). E-GPS first parses the geometric diagram and prob-lem text into unified formal language representations. Then, the answer and explainable reasoning and solving steps are obtained by a Top-Down Problem Solver (TD-PS), which innovatively solves the problem from the target and focuses on what is needed. To alleviate the data issues, a Bottom-Up Problem Generator (BU-PG) is devised to augment the data set with various well-annotated constructed geome-try problems. It enables us to train an enhanced theorem predictor with a better grasp of theorem knowledge, which further improves the efficiency ofTD-PS. Extensive experi-ments demonstrate that E-GPS maintains comparable solving performances with fewer steps and provides outstanding explainability.

关键词： Geometry Training computer vision Annotations Formal languages Generators Cognition

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SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-Modal Intent Detection

SDIF-DA: A Shallow-to-Deep Interaction Framework with Data A...

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

作者： Shijue Huang Libo Qin Bingbing Wang Geng Tu Ruifeng Xu School of Computer Science Harbin Institute of Technology (Shenzhen) Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Engineering Central South University Peng Cheng Laboratory

Multi-modal intent detection aims to utilize various modalities to understand the user’s intentions, which is essential for the deployment of dialogue systems in real-world scenarios. The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal intent training data. In this work, we introduce a shallow-to-deep interaction framework with data augmentation (SDIF-DA) to address the above challenges. Firstly, SDIF-DA leverages a shallow-to-deep interaction module to progressively and effectively align and fuse features across text, video, and audio modalities. Secondly, we propose a ChatGPT-based data augmentation approach to automatically augment sufficient training data. Experimental results demonstrate that SDIF-DA can effectively align and fuse multi-modal features by achieving state-of-the-art performance. In addition, extensive analyses show that the introduced data augmentation approach can successfully distill knowledge from the large language model.

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