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检索条件"机构=Fujian Key Laboratory of Sensing and Computing for Smart Cities"

共 356 条记录，以下是1-10 订阅

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FTP: A Human Pose Estimation Method Integrating Temporal and Fine-Grained Feature Fusion 16

FTP: A Human Pose Estimation Method Integrating Temporal and...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Cai, Shuqiang Ma, Chennan Wang, Xin Lin, Li Yan, Ming Lin, Xinchen Fan, Shuqi Shen, Siqi Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University Fujian Xiamen China

Human pose estimation is a significant research direction in the field of computer vision, with critical applications in human motion reconstruction and analysis. Currently proposed human pose estimation methods primarily focus on single-modality sensor information, such as RGB images and LiDAR point clouds. While these methods have achieved promising results within their respective domains, they remain limited by the inherent deficiencies of each modality, hindering their applicability across diverse real-world scenarios. With the recent introduction of numerous multi-modality human pose datasets, multi-modality approaches have begun to develop. However, existing multi-modality fusion methods mainly consider the global feature relationships between different modalities, without modeling finer-grained features or the dynamic temporal relationships between modalities. To address this issue, we propose a novel pipeline that integrates point cloud and image features, explicitly encoding fine-grained features and dynamic temporal relationships between the two modalities. Additionally, we employ a discriminator structure for semi-supervised training. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) performance compared to previous methods. © 2024 S. Cai, C. Ma, X. Wang, L. Lin, M. Yan, X. Lin, S. Fan & S. Shen.

关键词： Image coding

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ConDo: Continual Domain Expansion for Absolute Pose Regression 39

ConDo: Continual Domain Expansion for Absolute Pose Regressi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Zijun Cai, Zhipeng Yang, Bochun Shen, Xuelun Shen, Siqi Fan, Xiaoliang Paulitsch, Michael Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Intel Labs United States

ISBN: (纸本)157735897X

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after deployment. Training APR on a fixed dataset leads to overfitting, making it fail catastrophically on challenging novel data. This work proposes Continual Domain Expansion (ConDo), which continually collects unlabeled inference data to update the deployed APR. Instead of applying standard unsupervised domain adaptation methods which are ineffective for APR, ConDo effectively learns from unlabeled data by distilling knowledge from scene-agnostic localization methods. By sampling data uniformly from historical and newly collected data, ConDo can effectively expand the generalization domain of APR. Large-scale benchmarks with various scene types are constructed to evaluate models under practical (long-term) data changes. ConDo consistently and significantly outperforms baselines across architectures, scene types, and data changes. On challenging scenes (Fig. 1), it reduces the localization error by > 7x (14.8m vs 1.7m). Analysis shows the robustness of ConDo against compute budgets, replay buffer sizes and teacher prediction noise. Comparing to model retraining, ConDo achieves similar performance up to 25x faster. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Unsupervised learning

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HROF: a high-resolution remote sensing dataset for segmentation of offshore farms 5

HROF: a high-resolution remote sensing dataset for segmentat...

引用

5th International Conference on Geoscience and Remote sensing Mapping, ICGRSM 2023

作者： Qiu, Rihuang Cheng, Ming Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Fujian Xiamen361005 China

ISBN: (纸本)9781510672789

Deep learning (DL) methods have achieved promising results in high resolution (HR) remote sensing image (RSI) segmentation. While DL approaches rely heavily on large-scale training datasets, and a method trained on a dataset generally cannot be used to segment classes that the dataset does not contain. Existing RSI datasets mainly cover buildings, roads, vegetation, water, moving objects, etc. In this paper, we focus on a class of objects that have been rarely involved in previous datasets: offshore farms. The monitoring and management of offshore farms is important for the sustainable development of the aquaculture industry, and in this work, we provide an HR remote sensing dataset of offshore farms, named HROF. This dataset uses two types of sensors, i.e., multispectral and synthetic aperture radar (SAR). The multispectral data consists of 30 images with a resolution of about 1 m, including both raft and long line farms. The SAR data contains 41 images with a resolution of 3 m, in which the objects are breeding ponds. Objects in each image are manually annotated at the pixel-precise level. Furthermore, we provide baseline segmentation method and results on HROF and compare with mainstream methods. The dataset is available at https://***/FrontierQ/HROF. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image segmentation

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DiagnoCare: A Diagnostic Analysis System for Electronic Medical Records Combining Medical Knowledge Graph and Large Language Model 10

DiagnoCare: A Diagnostic Analysis System for Electronic Medi...

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10th IEEE smart World Congress, SWC 2024

作者： Huang, Shiwang Luo, Guofeng Wang, Jiaru Chen, Longbiao Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart Cities China Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China

ISBN: (纸本)9798331520861

With the rapid development of healthcare technologies and the increasing demand for intelligent diagnostic systems, we present a novel approach that combines medical knowledge graphs and Large Language Models (LLMs) for enhanced Electronic Medical Record (EMR) analysis and condition warning. Our system integrates structured medical knowledge with advanced text analysis capabilities to accurately interpret and process complex EMR data. By leveraging LLMs and prompt learning, the system provides personalized examination suggestions, diagnostic analyses, and disease warnings. Experimental results on real-world datasets, including the MIMIC III database and anonymized hospital records from fujian Province, China, demonstrate the system's efficacy in delivering precise and actionable insights. Case studies further illustrate the system's practical value and usability, highlighting its potential to significantly improve clinical decision-making and patient outcomes. This research offers substantial implications for the advancement of intelligent EMR systems and personalized healthcare. © 2024 IEEE.

关键词： Electronic health record

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Seg2Box: 3D Object Detection by Point-Wise Semantics Supervision 39

Seg2Box: 3D Object Detection by Point-Wise Semantics Supervi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zheng, Maoji Xu, Ziyu Xia, Qiming Wu, Hai Wen, Chenglu Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China

ISBN: (纸本)157735897X

LiDAR-based 3D object detection and semantic segmentation are critical tasks in 3D scene understanding. Traditional detection and segmentation methods supervise their models through bounding box labels and semantic mask labels. However, these two independent labels inherently contain significant redundancy. This paper aims to eliminate the redundancy by supervising 3D object detection using only semantic labels. However, the challenge arises due to the incomplete geometry structure and boundary ambiguity of point-cloud instances, leading to inaccurate pseudo-labels and poor detection results. To address these challenges, we propose a novel method, named Seg2Box. We first introduce a Multi-Frame Multi-Scale Clustering (MFMS-C) module, which leverages the spatio-temporal consistency of point clouds to generate accurate box-level pseudo-labels. Additionally, the Semantic-Guiding Iterative-Mining Self-Training (SGIM-ST) module is proposed to enhance the performance by progressively refining the pseudo-labels and mining the instances without generating pseudo-labels. Experiments on the Waymo Open Dataset and nuScenes Dataset show that our method significantly outperforms other competitive methods by 23.7% and 10.3% in mAP, respectively. The results demonstrate the great label-efficient potential and advancement of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Text to Point Cloud Localization with Multi-Level Negative Contrastive Learning 39

Text to Point Cloud Localization with Multi-Level Negative C...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Dunqiang Huang, Shujun Li, Wen Shen, Siqi Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China

ISBN: (纸本)157735897X

Language-based localization is a crucial task in robotics and computer vision, enabling robots to understand spatial positions through language. Recent methods rely on contrastive learning to establish correspondences between global features of texts and point clouds. However, the inherent ambiguity of textual descriptions makes it difficult to convey geometric information accurately, forcing alignment of them in the feature space may compromise the expressiveness of the point clouds. Unlike previous methods, this paper proposes using language as a filter to distinguish dissimilar locations. To this end, we propose a robust framework of multi-level negative contrastive learning for language-based localization, fully leveraging the descriptive power of language for spatial localization. Our method learns multiple mismatched factors by minimizing the similarity of different locations at different levels, including global-level, instance-level and relation-level, respectively. Extensive experiments conducted on the KITTI360Pose benchmark demonstrate that our method outperforms better that the state-of-the-art methods. Specifically, we achieve a 56.3% improvement in Top-1 retrieval recall and a 45.9% improvement in 5m localization recall. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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GIM: LEARNING GENERALIZABLE IMAGE MATCHER FROM INTERNET VIDEOS 12

GIM: LEARNING GENERALIZABLE IMAGE MATCHER FROM INTERNET VIDE...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Shen, Xuelun Cai, Zhipeng Yin, Wei Müller, Matthias Li, Zijun Wang, Kaixuan Chen, Xiaozhi Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University 361005 China Intel Labs United States DJI Technology China

Image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-wild images. Such methods typically need to train separate models for different scene types (e.g., indoor vs. outdoor) and are impractical when the scene type is unknown in advance. One of the underlying problems is the limited scalability of existing data construction pipelines, which limits the diversity of standard image matching datasets. To address this problem, we propose GIM, a self-training framework for learning a single generalizable model based on any image matching architecture using internet videos, an abundant and diverse data source. Given an architecture, GIM first trains it on standard domain-specific datasets and then combines it with complementary matching methods to create dense labels on nearby frames of novel videos. These labels are filtered by robust fitting, and then enhanced by propagating them to distant frames. The final model is trained on propagated data with strong augmentations. Not relying on complex 3D reconstruction makes GIM much more efficient and less likely to fail than standard SfM-and-MVS based frameworks. We also propose ZEB, the first zero-shot evaluation benchmark for image matching. By mixing data from diverse domains, ZEB can thoroughly assess the cross-domain generalization performance of different methods. Experiments demonstrate the effectiveness and generality of GIM. Applying GIM consistently improves the zero-shot performance of 3 state-of-the-art image matching architectures as the number of downloaded videos increases (Fig. 1 (a));with 50 hours of YouTube videos, the relative zero-shot performance improves by 6.9% - 18.1%. GIM also enables generalization to extreme cross-domain data such as Bird Eye View (BEV) images of projected 3D point clouds (Fig. 1 (c)). More importantly, our single zero-shot model consistently outperforms domain-s

关键词： Benchmarking

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AdaCo: Overcoming Visual Foundation Model Noise in 3D Semantic Segmentation via Adaptive Label Correction 39

AdaCo: Overcoming Visual Foundation Model Noise in 3D Semant...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zou, Pufan Zhao, Shijia Huang, Weijie Xia, Qiming Wen, Chenglu Li, Wei Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Inceptio United States

ISBN: (纸本)157735897X

Recently, Visual Foundation Models (VFMs) have shown a remarkable generalization performance in 3D perception tasks. However, their effectiveness in large-scale outdoor datasets remains constrained by the scarcity of accurate supervision signals, the extensive noise caused by variable outdoor conditions, and the abundance of unknown objects. In this work, we propose a novel label-free learning method, Adaptive Label Correction (AdaCo), for 3D semantic segmentation. AdaCo first introduces the Cross-modal Label Generation Module (CLGM), providing cross-modal supervision with the formidable interpretive capabilities of the VFMs. Subsequently, AdaCo incorporates the Adaptive Noise Corrector (ANC), updating and adjusting the noisy samples within this supervision iteratively during training. Moreover, we develop an Adaptive Robust Loss (ARL) function to modulate each sample's sensitivity to noisy supervision, preventing potential underfitting issues associated with robust loss. Our proposed AdaCo can effectively mitigate the performance limitations of label-free learning networks in 3D semantic segmentation tasks. Extensive experiments on two outdoor benchmark datasets highlight the superior performance of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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STKOpt: Automated Spatio-Temporal Knowledge Optimization for Traffic Prediction 25

STKOpt: Automated Spatio-Temporal Knowledge Optimization for...

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34th ACM Web Conference, WWW 2025

作者： Hong, Yayao Chen, Liyue Wang, Leye Xie, Xiuhuai Luo, Guofeng Wang, Cheng Chen, Longbiao Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University Xiamen China Ministry of Education School of Computer Science Peking University Beijing China

ISBN: (纸本)9798400712746

Ubiquitous sensors and mobile devices have spurred the growth of Web-of-Things (WoT) services in smart cities, making accurate spatio-temporal traffic predictions increasingly crucial. Leveraging advances in deep learning, recent Spatio-Temporal Graph Neural Networks (STGNNs) have achieved remarkable results. However, these methods address scenario-specific spatio-temporal heterogeneity by designing model architectures, often overlooking the importance of selecting optimal spatio-temporal knowledge (i.e., model inputs). In this paper, we propose an automated framework for spatio-temporal knowledge optimization to address this challenge. Our framework seamlessly integrates with downstream models, enhancing their performance across various prediction tasks. Specifically, we design a knowledge search space composed of parameters that represent scenario-specific spatio-temporal correlations within data. Additionally, we employ a bandit-based multi-fidelity algorithm for knowledge optimization to solve the constraint of limited resource. Furthermore, we adopt a meta-learner to extract transferable meta-knowledge about optimal knowledge, facilitating efficient exploration of the search space. Extensive experiments on five widely used real-world datasets demonstrate the effectiveness of our proposed framework. To the best of our knowledge, we are the first to automatically optimize spatio-temporal knowledge for spatio-temporal traffic prediction. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network 25

P4GCN: Vertical Federated Social Recommendation with Privacy...

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34th ACM Web Conference, WWW 2025

作者： Wang, Zheng Wang, Wanwan Huang, Yimin Peng, Zhaopeng Yang, Ziqi Yao, Ming Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Xiamen China Insightone Tech Co. Ltd Beijing China

ISBN: (纸本)9798400712746

In recent years, graph neural networks (GNNs) have been commonly utilized for social recommendation systems. However, real-world scenarios often present challenges related to user privacy and business constraints, inhibiting direct access to valuable social information from other platforms. While many existing methods have tackled matrix factorization-based social recommendations without direct social data access, developing GNN-based federated social recommendation models under similar conditions remains largely unexplored. To address this issue, we propose a novel vertical federated social recommendation method leveraging privacy-preserving two-party graph convolution networks (P4GCN) to enhance recommendation accuracy without requiring direct access to sensitive social information. First, we introduce a Sandwich-Encryption module to ensure comprehensive data privacy during the collaborative computing process. Second, we provide a thorough theoretical analysis of the privacy guarantees, considering the participation of both curious and honest parties. Extensive experiments on four real-world datasets demonstrate that P4GCN outperforms state-of-the-art methods in terms of recommendation accuracy. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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