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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

IEEE Transactions on Fuzzy Systems 2025年

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the lab.r-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlab.led data in complex and large-scale scenes is still challenging, such as only 20 points lab.led in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlab.led data learning through reliable long-range supervision propagation from lab.led superpoints to unlab.led superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlab.led superpoints to clusters centered on lab.led superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% lab.led points. Our code is availab.e at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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DRAUC: an instance-wise distributionally robust AUC optimization framework 23

DRAUC: an instance-wise distributionally robust AUC optimiza...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Siran Dai Qianqian Xu Zhiyong Yang Xiaochun Cao Qingming Huang SKLOIS Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and BDKM University of Chinese Academy of Sciences

The Area Under the ROC Curve (AUC) is a widely employed metric in long-tailed classification scenarios. Nevertheless, most existing methods primarily assume that training and testing examples are drawn i.i.d. from the same distribution, which is often unachievable in practice. Distributionally Robust Optimization (DRO) enhances model performance by optimizing it for the local worst-case scenario, but directly integrating AUC optimization with DRO results in an intractable optimization problem. To tackle this challenge, methodically we propose an instance-wise surrogate loss of Distributionally Robust AUC (DRAUC) and build our optimization framework on top of it. Moreover, we highlight that conventional DRAUC may induce lab.l bias, hence introducing distribution-aware DRAUC as a more suitable metric for robust AUC learning. Theoretically, we affirm that the generalization gap between the training loss and testing error diminishes if the training set is sufficiently large. Empirically, experiments on corrupted benchmark datasets demonstrate the effectiveness of our proposed method. Code is availab.e at: https://***/EldercatSAM/DRAUC.

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A unified generalization analysis of re-weighting and logit-adjustment for imbalanced learning 23

A unified generalization analysis of re-weighting and logit-...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Zitai Wang Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang SKLOIS Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and BDKM University of Chinese Academy of Sciences

Real-world datasets are typically imbalanced in the sense that only a few classes have numerous samples, while many classes are associated with only a few samples. As a result, a naïve ERM learning process will be biased towards the majority classes, making it difficult to generalize to the minority classes. To address this issue, one simple but effective approach is to modify the loss function to emphasize the learning on minority classes, such as re-weighting the losses or adjusting the logits via class-dependent terms. However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results. To bridge this gap, we propose a novel technique named data-dependent contraction to capture how these modified losses handle different classes. On top of this technique, a fine-grained generalization bound is established for imbalanced learning, which helps reveal the mystery of re-weighting and logit-adjustment in a unified manner. Furthermore, a principled learning algorithm is developed based on the theoretical insights. Finally, the empirical results on benchmark datasets not only validate the theoretical results but also demonstrate the effectiveness of the proposed method.

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Lifting the Answer: Reranking Candidates on Data Augmented Text-to-SQL

Lifting the Answer: Reranking Candidates on Data Augmented T...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Guang-Yu Cheng Shuo Yu Wen-Bin Jiang Shuai Ma Xiang Ao Qing He Henan Institutes of Advanced Technology Zhengzhou University Zhengzhou P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Baidu inc Beijing China

The text-to-SQL task is a realistic and challenging job, which aims to translate natural language questions into corresponding SQL queries. When attempting to generate SQL queries in the text-to-SQL task, prevailing semantic parsing models employ beam search to generate several candidates. Based on our pilot study, we observe that the gold-standard SQL answer may exist in the n-best candidate list produced by the decoder, rather than the first. Hence, we aim to lift the correct answer given several candidates generated by semantic parsing models for text-to-SQL. To this end, we propose a reranking module that reorders the n-best list of candidate SQL queries by pair-wise hinge loss. Meanwhile, a data augmentation module is leveraged to enrich the inadequate training instances for providing candidates with better qualities. These two modules can be easily grafted onto text-to-SQL backbone networks, and extensive experiments on the cross-domain text-to-SQL benchmark Spider demonstrate that our method achieves 73.8% in accuracy on the Spider dataset, surpassing the base model by up to 3.5%.

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HUMAN SIMULACRA: BENCHMARKING THE PERSONIFICATION OF LARGE LANGUAGE MODELS

arXiv

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arXiv 2024年

作者： Xie, Qiujie Feng, Qiming Zhang, Tianqi Li, Qingqiu Yang, Linyi Zhang, Yuejie Feng, Rui He, Liang Gao, Shang Zhang, Yue School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Tongji University China University College London United Kingdom Huawei Noah’s Ark Lab Canada Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention China Deakin University Australia Westlake University China Westlake Institute for Advanced Study China

Large Language Models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted the attention of the social science community, who see the potential in leveraging LLMs to replace human participants in experiments, thereby reducing research costs and complexity. In this paper, we introduce a benchmark for LLMs personification, including a strategy for constructing virtual characters’ life stories from the ground up, a Multi-Agent Cognitive Mechanism capable of simulating human cognitive processes, and a psychology-guided evaluation method to assess human simulations from both self and observational perspectives. Experimental results demonstrate that our constructed simulacra can produce personified responses that align with their target characters. We hope this work will serve as a benchmark in the field of human simulation, paving the way for future research. © 2024, CC BY-NC-SA.

关键词： Digital elevation model

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EFSA: Towards Event-Level Financial Sentiment Analysis

arXiv

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arXiv 2024年

作者： Chen, Tianyu Zhang, Yiming Yu, Guoxin Zhang, Dapeng Zeng, Li He, Qing Ao, Xiang Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China School of IoT Engineering Jiangsu Vocational College of Information Technology Wuxi China Information Technology Department I Shenzhen Stock Exchange China

In this paper, we extend financial sentiment analysis (FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictions, it has specialized challenges due to the lengthy and discontinuity of events in a financial text. To this end, we reconceptualize the event extraction as a classification task by designing a categorization comprising coarse-grained and finegrained event categories. Under this setting, we formulate the Event-Level Financial Sentiment Analysis (EFSA for short) task that outputs quintuples consisting of (company, industry, coarse-grained event, fine-grained event, sentiment) from financial text. A large-scale Chinese dataset containing 12, 160 news articles and 13, 725 quintuples is publicized as a brand new testbed for our task. A four-hop Chainof- Thought LLM-based approach is devised for this task. Systematically investigations are conducted on our dataset, and the empirical results demonstrate the benchmarking scores of existing methods and our proposed method can reach the current state-of-the-art. Our dataset and framework implementation are availab.e at https://***/cty1934/EFSA. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collab.rative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Knowledge Graph Embedding Based Graph Convolutional Network for Link Prediction

Knowledge Graph Embedding Based Graph Convolutional Network ...

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International Conference on advanced Cloud and Big Data (CBD)

作者： Qiang Wu Qing He Henan Institute of Advanced Technology Zhengzhou University Zhengzhou P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Science (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Science Beijing China

Knowledge Graph Embedding (KGE) aims to capture the inherent structural information within knowledge graphs (KGs) by means of representation learning. This is of paramount importance for various downstream tasks, including personalized recommendations, intelligent search, and relation extraction. Graph Convolutional Networks (GCNs), a type of neural network, have recently demonstrated significant success in modeling data structured as graphs. However, these models often overlook the intricate interrelation information between neighboring nodes, which can offer valuable insights for enhancing node embeddings. In this article, we present a knowledge graph embedding based graph convolutional network for link prediction. First, we utilize embeddings to represent entities and relations. Second, we perform circular correlation on subject embedding and relation embedding to generate two matrix and achieve full fusion of embedding at the element level. Third, we generate self-attention vectors using one of the matrices and perform the Hadamard product with the other matrix. Thereafter, new node features are generated through graph convolutional networks and residual unit. Finally, we use the scoring function to score the candidate triples. The experimental results demonstrate that our model achieves improved performance compared with a wide variety of baseline models.

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly availab.e dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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Rate-Splitting Multiple Access Enabled Green Probabilistic Semantic Communication over Wireless Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Xu, Ruopeng Yang, Zhaohui Mao, Yijie Huang, Chongwen Yang, Qianqian Xu, Lexi Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering China Hangzhou310027 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Zhejiang Prov. Key Lab. of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China China United Network Communications Corporation Research Institute Beijing100048 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China

In this paper, we propose a multi-user green probabilistic semantic communication (PSC) system for semantic communication (SemCom) facilitated by probabilistic knowledge graphs (PKGs), which are knowledge graphs (KGs) integrated with probability to represent semantic information. In the considered model, semantic information of different users is represented by PKGs. On this basis, we design a semantic compression model for multi-user downlink task-oriented SemCom, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Utilizing rate-splitting multiple access (RSMA) technology, the transmitted messages are splitted into shared and private ones. Considering the limited wireless resources and constraint in semantic communication, an optimization problem with the goal of minimizing system energy consumption comprehensively considering the computation and communication process is formulated. In order to address the optimization problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design with successive convex approximation (SCA) method, semantic compression ratio optimization with variable substitution method, and computation capacity allocation with obtaining closed-form optimal solution. Simulation results verify the effectiveness of the proposed algorithm. © 2017 IEEE.

关键词： Knowledge graph

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