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3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： He, Shanshan Song, Jinfang Zhang, Yonggang Tian, Xin Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University College of Software Changchun China Second Hospital of Jilin University Department of Pediatrics Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

Liver cancer is one of the liver diseases with a high morbidity and mortality. At present, surgery is still the main treatment method for liver cancer. Therefore, it is particularly important to accurately segment the liver on CT imaging. Deep learning-based algorithms struggle to derive shape priors due to the irregular shape of the liver and blurring organ borders in CT images. We present a two-stage 3D deep active contour model 3DAC based on the V-net and CV models, in which the neural network is guided to learn the global features by regressing the signed distance map. The signed distance function can also be used as an initialization level set function. Then, the active contour model is extended to three dimensions to enable evolution of the overall liver surface. The advantages of the proposed method for liver segmentation were validated on the AbdomenCT-lK dataset. Compared with the DALS method, the Dice coefficient and the Jaccard coefficient are improved by 4.29% and 4.58%, the ASD and 95HD indicators are improved by 5. 96mm and 10. 04mm, which proves the effectiveness and robustness of the proposed method. © 2022 IEEE.

关键词： Image segmentation

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NON-RADIATING ELASTIC SOURCES IN INHOMOGENEOUS ELASTIC MEDIA AT CORNERS WITH APPLICATIONS

arXiv

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arXiv 2025年

作者： Diao, Huaian Geng, Yueran Tang, Ruixiang School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Mathematics Jilin University Changchun130012 China Department of Mathematics City University of Hong Kong Kowloon Hong Kong

This paper is concerned with non-radiating elastic sources in inhomogeneous elastic media. We demonstrate that the value of non-radiating elastic sources must vanish at convex corners of their support, provided the sources exhibit Hölder continuous regularity near the corner. Additionally, their gradient must satisfy intricate algebraic relationships with the angles defining the underlying corners, assuming the sources have C1,α regularity with α ∈ (0, 1) in the neighborhood of the corners. To perform microlocal analysis around the corners, we employ the so-called complex geometrical optics (CGO) solutions as test functions within a partial differential system. These characterizations of non-radiating elastic sources in inhomogeneous elastic media at corners enable us to establish the unique identifiability results for determining the position and shape of radiating elastic sources by a single far-field measurement, both locally and globally. The uniqueness result by a single far-field measurement is a challenging problem with a colorful history in inverse scattering. Indeed, when the support of a radiating elastic source is a convex polygon, we can simultaneously determine the shape of the elastic source and its values at the corners, provided the source is Hölder continuous at the corner. Furthermore, when the source function exhibits C1,α regularity in the neighborhood of a corner, the gradient of the source function at that corner can also be generically *** Codes 35Q74, 35R30, 74B05, 86A22 © 2025, CC BY.

关键词： Geometrical optics

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Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers

Beyond Attentive Tokens: Incorporating Token Importance and ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sifan Long Zhen Zhao Jimin Pi Shengsheng Wang Jingdong Wang College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin China Baidu VIS University of Sydney

Vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we decouple the attentive and inattentive tokens. In addition to preserving the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%.

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MP: Endowing Large Language Models with Lateral Thinking 39

MP: Endowing Large Language Models with Lateral Thinking

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bai, Tian Cao, Yongwang Ge, Yan Yu, Haitao College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Graduate School of Comprehensive Human Sciences University of Tsukuba Japan Institute of Library Information and Media Science University of Tsukuba Japan

ISBN: (纸本)157735897X

The recent studies show that Large Language Models (LLMs) often fall short in tasks demanding creative, lateral thinking due to lacking a clear awareness of their own reasoning processes. To cope with this issue, we propose a novel metacognitive prompting method (titled as MP) by mimicking human metacognition. Through integrating metacognitive principles, MP endows LLMs with lateral thinking ability, thereby enhancing their abilities to strategize, monitor, and reflect on their responses when dealing with creative tasks. The experimental results with five base LLMs across three lateral thinking datasets demonstrate that: All LLMs armed with MP consistently outperform the representative baseline methods. For example, MP demonstrates superior performance over CoT prompting across Sentence Puzzle (+5.00%), Word Puzzle (+10.07%), BiRdQA (+6.48%), and RiddleSense (+2.65%) with GPT-3.5-turbo model. In particular, the deployment of MP with GPT-4 achieves significant performance improvements that even surpass human performance on BRAINTEASER benchmark, demonstrating the transformative potential of MP in enhancing the creative problem-solving abilities of LLMs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Spatiotemporal Transformer for Data Inference and Long Prediction in Sparse Mobile CrowdSensing

Spatiotemporal Transformer for Data Inference and Long Predi...

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

作者： En Wang Weiting Liu Wenbin Liu Chaocan Xiang Bo Yang 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 Chongqing University China

Mobile CrowdSensing (MCS) is a data sensing paradigm that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has been further proposed for large-scale and fine-grained sensing task with the advantage of collecting only a few data to infer unsensed data. However, in many real-world scenarios, such as early prevention of epidemic, people are interested in not only the data at the current, but also in the future or even long-term future, and the latter may be more important. Long-term prediction not only reduces sensing cost, but also identifies trends or other characteristics of the data. In this paper, we propose a spatiotemporal model based on Transformer to infer and predict the data with sparse sensed data by utilizing spatiotemporal relationships. We design a spatiotemporal feature embedding to embed the prior spatiotemporal information of sensing map into the model to guide model learning. Moreover, we also design a novel multi-head spatiotemporal attention mechanism to dynamically capture spatiotemporal relationships among data. Extensive experiments have been conducted on three types of typical urban sensing tasks, which verify the effectiveness of our proposed algorithms in improving the inference and long-term prediction accuracy with the sparse sensed data.

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A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection 29

A Coarse-to-fine Cascaded Evidence-Distillation Neural Netwo...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Yang, Zhiwei Ma, Jing Chen, Hechang Lin, Hongzhan Luo, Ziyang Chang, Yi College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Hong Kong Baptist University Hong Kong School of Artificial Intelligence International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Distillation

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Application of Hybrid Monocular SLAM Method in Augmented Reality 5

Application of Hybrid Monocular SLAM Method in Augmented Rea...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Zhang, Zuoming Wang, Hanwen Shu, Man Wang, Xin College of Software Engineering Jilin University Changchun Jilin130000 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin130000 China

In this paper, we design a hybrid (semi-direct) approach to simultaneous localization and mapping (SLAM) for monocular cameras and apply it to augmented reality (AR) for monocular cameras. We combine the advantagesof the direct method and the feature point method. We use both photometric bundle adjustment which is robust to camera exposure time and motion bundle adjustment which is geometrically robust based on feature points to do tracking process. This approach can maintain an intuitive direct local map as well as a reusable global sparse feature point map. Through the processing of point clouds, such as PCA plane detection and grid reconstruction, we greatly improve the effect of the augmented reality system. © Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

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Noisetrans: Point Cloud Denoising with Transformers

SSRN

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SSRN 2022年

作者： Hou, Guangzhe Qin, Guihe Sun, Minghui Liang, Yanhua Yan, Jie Zhang, Zhonghan Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University 2699 Chaoyang District Changchun Jilin130012 China

Introduction: Point clouds obtained from capture devices or 3D reconstruction techniques are often noisy and interfere with downstream ***: The paper aims to recover the underlying surface of noisy point ***: We design a novel model, NoiseTrans, which uses transformer encoder architecture for point cloud denoising. Specifically, we obtain structural similarity of point-based point clouds with the assistance of the transformer's core self-attention mechanism. By expressing the noisy point cloud as a set of unordered vectors, we convert point clouds into point embeddings and employ Transformer to generate clean point clouds. To make the Transformer preserve details when sensing the point cloud, we design the Local Point Attention to prevent the point cloud from being over-smooth. In addition, we also propose sparse encoding, which enables the Transformer to better perceive the structural relationships of the point cloud and improve the denoising ***: Experiments show that our model outperforms state-of-the-art methods in various datasets and noise environments. © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Application of Monocular Direct Vision Odometry in Augmented Reality 5

Application of Monocular Direct Vision Odometry in Augmented...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Zhang, Zuoming Wang, Zixuan Wang, Hanwen Wang, Xin College of Software Engineering Jilin University Changchun Jilin130000 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin130000 China

In recent years, the unlabeled augmented reality system has been gradually applied to various mobile devices, among which stable, accurate, and fast registration is the key to realizing this function. For this technique, this paper introduces camera exposure parameters and puts the data association and pose estimation into a unified nonlinear optimization problem. Moreover, the direct monocular vision odometer is transplanted into the augmented reality system through the position adjustment module. We compare it with the traditional visual odometry method that matches the feature points. The results show that this improved method can be used to track more quickly and build a more visual semi-dense point cloud map, which can be used to support the registration and tracking of virtual objects in augmented reality. © Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

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Skin Cancer Image Identification Using Deep Convolutional Neural Networks

Skin Cancer Image Identification Using Deep Convolutional Ne...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Zhang, Wenbo Sun, Xiaolin Ma, Shiyi Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

Skin cancer is a type of cancer and has emerged as one of the most prevalent types over the past decade. Early diagnosis and treatment are a key factor in the treatment of skin cancer. The traditional clinical diagnosis method of skin cancer is dermoscopy. On account of the complexity of the skin image, occasional instances of missed diagnosis and misdiagnosis arise, leading to delayed optimal treatment for patients. Two different networks, Inception-V3 and ResNet-50, are put forward in this paper. The dataset is separated into two categories, and the network performance is evaluated using evaluation indicators. Findings indicate that the ResNet50 network attains a higher accuracy rate of 88.83% compared to the Inception-V3 network's 83.17% accuracy. Additionally, the other three indicators of ResNet-50 are also surpass those of Inception-V3. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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