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arXiv 2024年

作者： Zhang, Xiaoli Wang, Liying Zhao, Libo Li, Xiongfei Ma, Siwei The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China

Multi-modality image fusion aims at fusing modality-specific (complementarity) and modality-shared (correlation) information from multiple source images. To tackle the problem of the neglect of inter-modal information, high-frequency information loss and limited attention to downstream tasks, this paper focuses on how to model correlation-driven decomposing features and reason high-level graph representation by efficiently extracting complementary features and aggregating multi-guided features. We propose a three-branch encoder-decoder architecture along with corresponding fusion layers as the fusion strategy. The transformer with Multi-Dconv Head Transposed Attention and Local-enhanced Feed Forward network is used to extract shallow features after the depthwise convolution. In the three parallel branches of the encoder, Cross Attention and Invertible Block (CAI) enables to extract local features and preserve high-frequency texture details. Base Feature Extraction Module (BFE) with residual connections can capture long-range dependencies and enhance modality-shared information. Graph Reasoning Module (GR) is introduced to reason high-level cross-modality relations and simultaneously extract low-level detail features as CAI’s modality-specific complementary information. Experiments demonstrate that our method obtains competitive results compared with state-of-the-art methods in visible/infrared image fusion and medical image fusion tasks. Moreover, we surpass other fusion methods in terms of subsequent tasks, averagely scoring 8.27% mAP@0.5 higher in object detection and 5.85% mIoU higher in semantic segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Few-Shot Fine-Grained Classification of Histological Images

Few-Shot Fine-Grained Classification of Histological Images

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yingdong Jiang Jing Huang Zihe Jin Leqi Shen Ziyi Zhang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Software Jilin University Jilin University ChangChun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin University ChangChun China College of Computer Science and Technology Jilin University ChangChun China

Histological image classification plays a crucial role in cancer diagnosis. However, the acquisition of well-labeled histological images is prohibitively expensive, and obtaining rare abnormal samples is challenging. Therefore, applying few-shot learning methods to histological image classification tasks holds significant clinical value. Nevertheless, existing research predom-inantly relies on coarse-grained image classification approaches based on natural image datasets, which struggle to address the fine-grained challenges encountered in histological image classification, such as intra-class diversity and inter-class similarity. To tackle this issue, this study proposes a novel few-shot fine-grained classification method for histological images, named “Category-Aware Feature Map Reconstruction Network.” This method employs channel weights to localize the differences between inter-class and intra-class regions, composed of intra-class channel weights and inter-class channel weights, collectively referred to as category-aware weights. Specifically, intra-class channel weights indicate the matching degree of salient regions within the support set of a particular class, while inter-class channel weights represent the degree of containing distinct information between classes. The category-aware weights are utilized to transform the support feature maps and query feature maps, generating feature maps that capture differentiating details between categories. Finally, the distance between the transformed query feature map and support feature map is calculated to achieve probabilistic predictions for the categories. On a histological few-shot dataset, this method achieves an accuracy of 90.23% using ResNet-12 as the feature extractor, surpassing the baseline model by 5.24% and outperforming other few-shot methods by at least 10% in the 5-way 10-shot experimental setting. The proposed method exhibits exceptional performance on histological image few-shot datasets, playing a

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Enhancing Nested Named Entity Recognition in Chinese EMRs: A Collaborative Approach

Enhancing Nested Named Entity Recognition in Chinese EMRs: A...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongtao Yue Xiaoyue Feng Hanchi Xu Haoxuan Chu Yushi Wang Renchu Guan Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Cardiovascular Center The First Hospital of Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Named entity recognition (NER) in electronic medical records (EMRs) is critical for identifying medical entities, constructing medical knowledge graphs, and supporting clinical decision-making. However, the scarcity of EMR datasets and the challenges posed by the complexity of Chinese medical texts hinder progress in this area. To address these issues, we introduce CMR-NER, a nested named entity recognition model that enhances entity prediction by integrating multiple features and considering a global view of entity boundaries. Leveraging the robust generalization capabilities of the large language model, CMR-NER involves collaboration with medical experts to ensure accuracy and reduce annotation costs. Additionally, we present HF-NER, a high-quality dataset specifically curated for Chinese EMR entity recognition focusing on heart failure. This dataset, constructed through a methodology combining ChatGPT’s capabilities and expert validation, is a significant contribution to the field. It facilitates a comprehensive evaluation of CMR-NER. Experimental results demonstrate that our approach achieves comparable or superior performance to existing methods, underscoring its effectiveness in this challenging domain.

关键词： Costs Annotations Large language models Collaboration Named entity recognition Medical services knowledge graphs Predictive models Chatbots Cardiovascular diseases

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Affine Invariant Feature Based on Local Color

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Chinese Journal of Electronics 2023年第3期22卷 511-516页

作者： LIU Pingping ZHOU Qiuzhan ZHAO Hongwci School of Computer Science and Technology Jilin University Changchun China Ministry of Education Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China School of Communication Engineering Jilin University Changchun China

Image localization using local features has attracted a lot of attention in recent mobile robots research. A novel, fast local invariant feature in affine transformation is proposed in this article, called AIFLC (Affine invariant feature based on local color). We adopt affine moment invariants to build affine invariant descriptors. Moreover, we use color gradient based center moment instead of original pixel values in order to enhance discriminative power and robustness of descriptor in photometric transformations. Simulation results show that the run time of AIFLC using optimal selection parameters is about 1/3 of classical SIFT algorithm. Using the standard evaluation images and the ones taken by mobile robots, we experimentally demonstrate that the AIFLC outperforms the state-of-art approaches such as SIFT and SURF in terms of image scaling, rotation, viewpoint changing, and blur transformations.

关键词： Location awareness Image color analysis Navigation Simulation Lighting Real-time systems Robustness

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LLM-Powered User Simulator for Recommender System

arXiv

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arXiv 2024年

作者： Zhang, Zijian Liu, Shuchang Liu, Ziru Zhong, Rui Cai, Qingpeng Zhao, Xiangyu Zhang, Chunxu Liu, Qidong Jiang, Peng Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Kuaishou Technology China City University of Hong Kong Hong Kong Xi’an Jiaotong University China

User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerating their iteration and optimization. However, prevalent user simulators generally suffer from significant limitations, including the opacity of user preference modeling and the incapability of evaluating simulation accuracy. In this paper, we introduce an LLM-powered user simulator to simulate user engagement with items in an explicit manner, thereby enhancing the efficiency and effectiveness of reinforcement learning-based recommender systems training. Specifically, we identify the explicit logic of user preferences, leverage LLMs to analyze item characteristics and distill user sentiments, and design a logical model to imitate real human engagement. By integrating a statistical model, we further enhance the reliability of the simulation, proposing an ensemble model that synergizes logical and statistical insights for user interaction simulations. Capitalizing on the extensive knowledge and semantic generation capabilities of LLMs, our user simulator faithfully emulates user behaviors and preferences, yielding high-fidelity training data that enrich the training of recommendation algorithms. We establish quantifying and qualifying experiments on five datasets to validate the simulator’s effectiveness and stability across various recommendation scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion 39

Skeleton-based Action Recognition with Non-linear Dependency...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Haipeng Yang, Yuheng Lyu, Yingda College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Public Computer Education and Research Center Jilin University China

ISBN: (纸本)157735897X

Human skeleton-based action recognition has long been an indispensable aspect of artificial intelligence. Current state-of-the-art methods tend to consider only the dependencies between connected skeletal joints, limiting their ability to capture non-linear dependencies between physically distant joints. Moreover, most existing approaches distinguish action classes by estimating the probability density of motion representations, yet the high-dimensional nature of human motions invokes inherent difficulties in accomplishing such measurements. In this paper, we seek to tackle these challenges from two directions: (1) We propose a novel dependency refinement approach that explicitly models dependencies between any pair of joints, effectively transcending the limitations imposed by joint distance. (2) We further propose a framework that utilizes the Hilbert-Schmidt Independence Criterion to differentiate action classes without being affected by data dimensionality, and mathematically derive learning objectives guaranteeing precise recognition. Empirically, our approach sets the state-of-the-art performance on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning

arXiv

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arXiv 2025年

作者： Liu, Yonghao Li, Mengyu Pang, Wei Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University China Mathematical and Computer Sciences Heriot-Watt University United Kingdom University of Trento Italy

Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source information (i.e., statistical information, linguistic information, and factual information) exploration to alleviate the sparsity issues. Then, the graph learning approach is adopted to learn the representation of short texts, which are presented in graph forms. Moreover, we introduce a dual-level (i.e., instance-level and cluster-level) contrastive learning auxiliary task to effectively capture different-grained contrastive information within massive unlabeled data. Meanwhile, previous models merely perform the main task and auxiliary tasks in parallel, without considering the relationship among tasks. Therefore, we introduce a hierarchical architecture to explicitly model the correlations between tasks. We conduct extensive experiments across various benchmark datasets, demonstrating that MI-DELIGHT significantly surpasses previous competitive models. It even outperforms popular large language models on several datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Few-Shot Data Completion for New Tasks in Sparse Crowdsensing

Few-Shot Data Completion for New Tasks in Sparse Crowdsensin...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： En Wang Mijia Zhang Bo Yang Yang Xu Zixuan Song Yongjian Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Electronic Engineering Hunan University China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Mobile Crowdsensing is a type of technology that utilizes mobile devices and volunteers to gather data about specific topics at large scales in real-time. However, in practice, limited participation leads to missing data, i.e., the collected data may be sparse, which makes it difficult to perform accurate analysis. A possible technique called sparse crowdsensing incorporates the sparse case with data completion, where unsensed data could be estimated through inference. However, sparse crowdsensing typically suffers from poor performance during the data completion stage due to various challenges: the sparsity of the sensed data, reliance on numerous timeslots, and uncertain spatiotemporal connections. To resolve such few-shot issues, the proposed solution uses the Correlated Data Fusion for Matrix Completion (CDFMC) approach, which leverages a small amount of objective data to retrain an auxiliary dataset-based pre-trained model that can estimate unsensed data efficiently. CDFMC is trained using a combination of the traditional Deep Matrix Factorization and the Kalman Filtering, which not only enables the efficient representation and comparison of data samples but also fuses the objective data and auxiliary data effectively. Evaluation results show that the proposed CDFMC outperforms baseline techniques, achieving high accuracy in completing unsensed data with minimal training data.

关键词： Performance evaluation Accuracy Crowdsensing Filtering Training data Data models Real-time systems

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A Method for Extracting Electronic Medical Record Entities by Fusing Multichannel Self-Attention Mechanism with Location Relationship Features 9th

A Method for Extracting Electronic Medical Record Entities b...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Xu, Hongyan Wang, Hong Feng, Yong Wang, Rongbing Zhang, Yonggang College of Information Liaoning University Shenyang110036 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819959709

With the implementation of the "Internet+" strategy, electronic medical records are generally applied in the medical field. Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients’ states, but the existing methods for extracting entities from electronic medical records have problems of redundant information, overlapping entities, and low accuracy rates. Therefore, this paper proposes an entity extraction method for electronic medical records based on the network framework of BERT-BiLSTM, which incorporates a multichannel self-attention mechanism and location relationship features. First, the text input sequence was encoded using the BERT-BiLSTM network framework, and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mechanism. Then, the position relation characteristic was used to extract the local semantic message of the text, and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained. Next, the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix. Finally, the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus, and the experimental results indicate that the proposed method surpasses existing methods in terms of precision, recall, F1 value and training time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Location

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A Morphology Focused Cell Detection Model for Histopathology Images

A Morphology Focused Cell Detection Model for Histopathology...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhe Wang Fangyue Wei Shuyu Guo Xiaoting Che 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

The accurate automatic recognition of cell locations is of great significance for downstream tasks in pathology. Due to the various size and distribution of different cell types, previous cell detection methods applied fixed circles as labels to localize cell position by default, which makes less precise and more difficult to adapt various cell morphology. In this paper, we integrate adaptive areas of interests based on morphology to address the limitation in detecting cells with irregular shapes in pathological images. Moreover, a EGSI module is proposed to extract global distribution of cells in slices, which enables model to acquire more rich semantic information. We exhibit favorable F 1 score of our method on four public datasets. Experimental results demonstrate that the proposed method could detect cells more accurately in those images with different cell morphologies and dense cell distribution.

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