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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Lu, Wanli Zhang, Wen Sun, Mingrui Zhang, Jindong College of Computer Science and Technology Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819721436

Visual perception plays an important role in autonomous driving technology. The two key factors in visual perception tasks are monocular object detection and structured data analysis. In this paper, a structured data parsing algorithm based on monocular vision is proposed. Firstly, using the single-stage YOLOv5 object detection model, the target was located and its kind was identified. Secondly, the 3D position of the object in the camera coordinate system is calculated according to the mapping relationship between the imaging point and the target point. Finally, the distance between the object and the camera is calculated by using the pinhole camera model, and the size of the object and the horizontal declination Angle relative to the optical center of the camera are estimated by geometric derivation. This method not only has a simple principle, but also guarantees the precision of structured data output. Experimental findings support the method's viability and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Caption-Aware Multimodal Relation Extraction with Mutual Information Maximization 24

Caption-Aware Multimodal Relation Extraction with Mutual Inf...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Zefan Zhang, Weiqi Li, Yanhui Bai, Tian College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

ISBN: (纸本)9798400706868

Multimodal Relation Extraction (MRE) has achieved great improvements. However, modern MRE models are easily affected by irrelevant objects during multimodal alignment which are called error sensitivity issues. The main reason is that visual features are not fully aligned with textual features and the reasoning process may suppress redundant and noisy information at the risk of losing critical information. In light of this, we propose a Caption-Aware Multimodal Relation Extraction Network with Mutual Information Maximization (CAMIM). Specifically, we first generate detailed image captions through the Large Language Model (LLM). Then, the Caption-Aware Module (CAM) hierarchically aligns the fine-grained visual entities and textual entities for reasoning. In addition, for preserving crucial information within different modalities, we leverage a Mutual Information Maximization method to regulate the multimodal reasoning module. Experiments show that our model outperforms the state-of-the-art MRE models on the benchmark dataset MNRE. Further ablation studies prove the pluggable and effective performance of our Caption-Aware Module and Mutual Information Maximization method. Our code is available at https://***/zefanZhang-cn/CAMIM. © 2024 ACM.

关键词： Modeling languages

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Harmfully Manipulated Images Matter in Multimodal Misinformation Detection 24

Harmfully Manipulated Images Matter in Multimodal Misinforma...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Bing Wang, Shengsheng Li, Changchun Guan, Renchu Li, Ximing College of Computer Science and Technology Jilin University Jilin Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin University China

ISBN: (纸本)9798400706868

Nowadays, misinformation is widely spreading over various social media platforms and causes extremely negative impacts on society. To combat this issue, automatically identifying misinformation, especially those containing multimodal content, has attracted growing attention from the academic and industrial communities, and induced an active research topic named Multimodal Misinformation Detection (MMD). Typically, existing MMD methods capture the semantic correlation and inconsistency between multiple modalities, but neglect some potential clues in multimodal content. Recent studies suggest that manipulated traces of the images in articles are non-trivial clues for detecting misinformation. Meanwhile, we find that the underlying intentions behind the manipulation, e.g., harmful and harmless, also matter in MMD. Accordingly, in this work, we propose to detect misinformation by learning manipulation features that indicate whether the image has been manipulated, as well as intention features regarding the harmful and harmless intentions of the manipulation. Unfortunately, the manipulation and intention labels that make these features discriminative are unknown. To overcome the problem, we propose two weakly supervised signals as alternatives by introducing additional datasets on image manipulation detection and formulating two classification tasks as positive and unlabeled learning problems. Based on these ideas, we propose a novel MMD method, namely Harmfully Manipulated Images Matter in MMD (Hami-m3d). Extensive experiments across three benchmark datasets can demonstrate that Hami-m3d can consistently improve the performance of any MMD baselines. © 2024 ACM.

关键词： image manipulation

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Few-Shot Fine-Grained Classification of Histological Images 1

Few-Shot Fine-Grained Classification of Histological Images

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

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

ISBN: (纸本)9798350358780

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

关键词： Computer aided diagnosis

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More Flexible Proximity Wildcards Path Planning with Compressed Path Databases 34

More Flexible Proximity Wildcards Path Planning with Compres...

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34th International Conference on Automated Planning and Scheduling, ICAPS 2024

作者： Chen, Xi Zhang, Yue Zhang, Yonggang College of Software Jilin University China College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

ISBN: (纸本)9781577358893

Grid-based path planning is one of the classic problems in AI, and a popular topic in application areas such as computer games and robotics. Compressed Path Databases (CPDs) are recognized as a state-of-the-art method for grid-based path planning. It is able to find an optimal path extremely fast without state-space search. In recent years, researchers have tended to focus on improving CPDs by reducing CPD size or improving search performance. Among various methods, proximity wildcards are one of the most proven improvements in reducing the size of CPD. However, its proximity area is significantly restricted by complex terrain, which significantly affects the pathfinding efficiency and causes more additional costs. In this paper, we enhance CPDs from the perspective of improving search efficiency and reducing search costs. Our work focuses on using more flexible methods to obtain larger proximity areas, so that more heuristic information can be used to improve search performance. Experiments conducted on the Grid-Based Path Planning Competition (GPPC) benchmarks demonstrate that the two proposed methods can effectively improve search efficiency and reduce search costs by up to 3 orders of magnitude. Remarkably, our methods can further reduce the storage cost, and improve the compression capability of CPDs simultaneously. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Motion planning

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In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought 41

In-Context Decision Transformer: Reinforcement Learning via ...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Sili Hu, Jifeng Chen, Hechang Sun, Lichao Yang, Bo School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Lehigh University BethlehemPA United States

In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-improvement not requiring gradient updates, current works still suffer from high computational costs when the across-episodic sequence increases with task horizons. To this end, we propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. Specifically, IDT is inspired by the efficient hierarchical structure of human decision-making and thus reconstructs the sequence to consist of high-level decisions instead of low-level actions that interact with environments. As one high-level decision can guide multi-step low-level actions, IDT naturally avoids excessively long sequences and solves online tasks more efficiently. Experimental results show that IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods. In particular, the online evaluation time of our IDT is 36× times faster than baselines in the D4RL benchmark and 27× times faster in the Grid World benchmark. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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Local feature aggregation algorithm based on graph convolutional network

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Frontiers of Computer Science 2022年第3期16卷 203-205页

作者： Hao WANG Liyan DONG Minghui SUN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China

1Introduction and main contributions In the field of social networks and knowledge graphs,semi-supervised learning models based on graph convolutional networks have achieved great success in node classification[1],inductive node embedding[2],link prediction[3],and *** semi-supervised models based on graph convolutional network(GCN)[4]expect to obtain more feature information of a graph or accelerate the training.

关键词： convolution aggregation semi

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Migration: An Efficient Explorer Operator to Guide Swarm Evolution

Migration: An Efficient Explorer Operator to Guide Swarm Evo...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiaosong Han Chengyang Zhou Yanchun Liang Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Zhuhai College of Science and Technology Zhuhai China

Particle Swarm Optimization with Migration (MPSO) is proposed to solve the issue that PSO will encounter unbearable time cost problems when dealing with High-dimension, Expensive and Black-box objective function tasks. The Migration operator is inspired by the migration of Salmon. Salmon will start a dangerous journey from the ocean to the home rivers for reproduction. The process of the entire behavior is similar to the reduction and recovery of dimension. Therefore, we design the Migration operator where a pretrained Wasserstein Autoencoders (WAE) is applied to simulate the migration behavior to accelerate the process of evolution in PSO, and we use Least-Squares Regression in lower space to produce better generation. In comparison with famous baseline methods in some benchmark functions, MPSO converges faster and more accurately, which shows the great potential of migration operations.

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A Simple but Effective Approach for Unsupervised Few-Shot Graph Classification 24

A Simple but Effective Approach for Unsupervised Few-Shot Gr...

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33rd ACM Web Conference, WWW 2024

作者： Liu, Yonghao Huang, Lan Cao, Bowen Li, Ximing Giunchiglia, Fausto Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400701719

Graphs, as a fundamental data structure, have proven efficacy in modeling complex relationships between objects and are therefore found in wide web applications. Graph classification is an essential task in graph data analysis, which can effectively assist in extracting information and mining content from the web. Recently, few-shot graph classification, a more realistic and challenging task, has garnered great research interest. Existing few-shot graph classification models are all supervised, assuming abundant labeled data in base classes for meta-training. However, sufficient annotation is often challenging to obtain in practice due to high costs or demand for expertise. Moreover, they commonly adopt complicated meta-learning algorithms via episodic training to transfer prior knowledge from base classes. To break free from these constraints, in this paper, we propose a simple yet effective approach named SMART for unsupervised few-shot graph classification without using any labeled data. SMART employs transfer learning philosophy instead of the previously prevailing meta-learning paradigm, avoiding the need for sophisticated meta-learning algorithms. Additionally, we adopt a novel mixup strategy to augment the original graph data and leverage unsupervised pretraining on these data to obtain the expressive graph encoder. We also utilize the prompt tuning technique to alleviate the overfitting and low fine-tuning efficiency caused by the limited support samples of novel classes. Extensive experimental results demonstrate the superiority of our proposed approach, significantly surpassing even leading supervised few-shot graph classification models. Our code is available here. © 2024 ACM.

关键词： Graph neural networks

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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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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

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

ISBN: (纸本)9798350386226

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. © 2024 IEEE.

关键词： Electronic health record

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