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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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Research on Human Eyesight Tracking Algorithm Based on Monocular Vision 2nd

Research on Human Eyesight Tracking Algorithm Based on Monoc...

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

作者： Tang, Haochun Zhang, Jindong Yang, Jing College of Software Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819970100

This paper focuses on the optimization and improvement of the line-of-sight tracking algorithm based on monocular vision, and aims to achieve a series of complex functions through image analysis based on line-of-sight tracking technology and more in-depth analysis of monocular images. Therefore, we use the webgaze framework and use face detection models and feature point recognition libraries to analyze the recognition rate and provide further development of the technology. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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SS-MAF: Semi-Supervised Echocardiographic Segmentation Using Multi-Atlas Fusion

SS-MAF: Semi-Supervised Echocardiographic Segmentation Using...

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Communication, Image and Signal Processing (CCISP), International Conference on

作者： Shuaihua Yang Caixia Zheng College of Information Science and Technology Northeast Normal University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Left ventricle (LV) segmentation in echocardiography is of paramount significance in cardiac function analysis. Recently, semi-supervised segmentation has garnered considerable attention due to its potential mitigation of labeling costs. Despite this, most semi-supervised methodologies tend to overlook the specialized anatomical priors specific to echocardiography, often yielding suboptimal outcomes. Simultaneously, atlas priors have been validated on other organs and imaging modalities. However, their direct application in echocardiography is fraught with challenges, primarily attributed to the indistinct boundaries of the LV and the presence of fan-shaped image edges, which impede atlas image registration. This paper presents a new method of semi-supervised segmentation using multi-atlas fusion, named SS- MAF, which is designed to incorporate atlas priors tailored for echocardiography while circumventing the intricate image registration process in traditional atlas-based methods. The SS-MAF comprises two components: a Multi-Task Learning (MTL) module and a Multi-Atlas Fusion (MAF) module. The MTL module serves precise landmark localization and segmentation estimation. The MAF module is primarily focused on the generation of probabilistic atlases, utilizing annotated atlas data with the guidance of key landmarks to facilitate the automatic annotation of unlabeled data, avoiding the traditional image-level registration, thus enhancing the performance of the MTL module. Our SS- MAF is rigorously evaluated on the publicly available CAMUS dataset, with results demonstrating its superiority over other state-of-the-art semi-supervised segmentation techniques in echocardiography.

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Troublemaker Learning for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Song, Yinghao Cao, Zhiyuan Xiang, Wanhong Long, Sifan Ge, Hongwei Liang, Yanchun Yang, Bo Wu, Chunguo College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin China YGSOFT INC Dalian University of Technology China

Low-light image enhancement (LLIE) restores the color and brightness of underexposed images. Supervised methods suffer from high costs in collecting low/normal-light image pairs. Unsupervised methods invest substantial effort in crafting complex loss functions. We address these two challenges through the proposed TroubleMaker Learning (TML) strategy, which employs normal-light images as inputs for training. TML is simple: we first dim the input and then increase its brightness. TML is based on two core components. First, the troublemaker model (TM) constructs pseudo low-light images from normal images to relieve the cost of pairwise data. Second, the predicting model (PM) enhances the brightness of pseudo low-light images. Additionally, we incorporate an enhancing model (EM) to further improve the visual performance of PM outputs. Moreover, in LLIE tasks, characterizing global element correlations is important because more information on the same object can be captured. CNN cannot achieve this well, and self-attention has high time complexity. Accordingly, we propose Global Dynamic Convolution (GDC) with O(n) time complexity, which essentially imitates the partial calculation process of self-attention to formulate elementwise correlations. Based on the GDC module, we build the UGDC model. Extensive quantitative and qualitative experiments demonstrate that UGDC trained with TML can achieve competitive performance against state-of-the-art approaches on public datasets. The code is available at https://***/Rainbowman0/TML LLIE. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean Optimization

arXiv

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arXiv 2023年

作者： Jiang, Luyu Ouyang, Dantong Zhang, Qi Zhang, Liming College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

Local search is an effective method for solving large-scale combinatorial optimization problems, and it has made remarkable progress in recent years through several subtle mechanisms. In this paper, we found two ways to improve the local search algorithms in solving Pseudo-Boolean Optimization(PBO): Firstly, some of those mechanisms such as unit propagation are merely used in solving MaxSAT before, which can be generalized to solve PBO as well;Secondly, the existing local search algorithms utilize the heuristic on variables, so-called score, to mainly guide the search. We attempt to gain more insights into the clause, as it plays the role of a middleman who builds a bridge between variables and the given formula. Hence, we first extended the combination of unit propagation-based decimation algorithm to PBO problem, giving a further generalized definition of unit clause for PBO problem, and apply it to the existing solver LS-PBO for constructing an initial assignment;then, we introduced a new heuristic on clauses, dubbed care, to set a higher priority for the clauses that are less satisfied in current iterations. Experiments on benchmarks from the most recent PB Competition, as well as three real-world application benchmarks including minimum-width confidence band, wireless sensor network optimization, and seating arrangement problems show that our algorithm DeciLS-PBO has a promising performance compared to the state-of-the-art algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Wireless sensor networks

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Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection 39

Uncertainty-Aware Global-View Reconstruction for Multi-View ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hao, Pingting Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University Portland 97201 OR United States

ISBN: (纸本)157735897X

In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Robust Federated Semi-Supervised Learning for Medical Image Classification via Pseudo-Label Filtering

Robust Federated Semi-Supervised Learning for Medical Image ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziwei Wang Shuyu Guo Mingzhu Zhu Tian Bai College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical institutions, FL methods usually suffer from insufficient data annotations and irrelevant noise within private datasets. To address these issues, a robust federated semi-supervised learning method via pseudo-label filtering (PFRFed) is introduced to utilize unlabeled data while mitigating the impact of noise data. Compared with existing federated semi-supervised learning methods, we propose a pseudo-label filtering mechanism with double dynamic thresholds, which allows the model to adopt more unlabeled data by adjusting the confidence and entropy thresholds at each stage of model training. Moreover, to reduce the degradation caused by noise data in private datasets from different clients, a noise-tolerant loss function and a grouping aggregation method based on the local model similarity are employed. The comparative experiments demonstrate the effectiveness of PFRFed, which has achieved the best classification accuracy of 95.20% and 88.72% on two public medical datasets. Also, PFRFed exhibits heightened resilience to variations in noisy data ratio and labeled data ratio, reaffirming its versatility and robustness.

关键词： Training Filtering Biological system modeling Noise Semisupervised learning Data models Robustness Noise measurement Biomedical imaging Image classification

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Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection

arXiv

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arXiv 2025年

关键词： Feature Selection

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Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection 39

Reconsidering Feature Structure Information and Latent Space...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pan, Hanlin Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University Portland 97201 OR United States

ISBN: (纸本)157735897X

The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguation, previous methods mainly focus on utilizing the information inside the labels and the relationship between the labels and features. However, the information existing in the feature space is rarely considered, especially in partial multi-label scenarios where the noises is considered to be concentrated in the label space while the feature information is correct. This paper proposes a method based on latent space alignment, which uses the information mined in feature space to disambiguate in latent space through the structural consistency between labels and features. In addition, previous methods overestimate the consistency of features and labels in the latent space after convergence. We comprehensively consider the similarity of latent space projections to feature space and label space, and propose new feature selection term. This method also significantly improves the positive label identification ability of the selected features. Comprehensive experiments demonstrate the superiority of the proposed method. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Labeled data

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Boosting few-shot point cloud segmentation with intra-class correlation and iterative prototype fusion

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Computer Vision and Image Understanding 2025年 258卷

作者： Zhang, Xindan Li, Ying Zhang, Xinnian 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 of Information and Communication Technology Ajou University Korea Republic of

Semantic segmentation of 3D point clouds is often limited by the challenge of obtaining labeled data. Few-shot point cloud segmentation methods, which can learn previously unseen categories, help reduce reliance on labeled datasets. However, existing methods are susceptible to correlation noise and suffer from significant discrepancies between support prototypes and query features. To address these issues, we first introduce an intra-class correlation enhancement module for filtering correlation noise driven by inter-class similarity and intra-class diversity. Second, to better represent the target classes, we propose an iterative prototype fusion module that adapts the query point cloud feature space, mitigating the problem of object variations in the support set and query set. Extensive experiments on S3DIS and ScanNet benchmark datasets demonstrate that our approach achieves competitive performance with state-of-the-art methods. © 2025 Elsevier Inc.

关键词： Semantic Segmentation

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