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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Liu, Ting Wang, Yong Xiang, Jianhong Wang, Ying Liu, Yang College Of Information And Communication Engineering Harbin Engineering University Harbin150001 China Sichuan Key Laboratory Of Agile Intelligent Computing Southwest China Institute Of Electronics Technology Chengdu610036 China

ISBN: (纸本)9798400709272

Millimeter-wave (mmWave) and massive Multiple Input Multiple Output (MIMO) technologies rely heavily on accurate channel state information (CSI) to significantly increase system capacity. However, the large size of the antenna array makes it more difficult to obtain accurate channel state information. We propose to utilize beam domain channels to improve the channel estimation accuracy. Specifically, we first construct the angular domain compensation. By utilizing this angular domain compensation, we further propose an angular domain compensated alternating direction multiplier method (ADC-ADMM) algorithm. The scheme models the channel estimation problem as a bi-objective convex optimization problem with joint angular domain compensation, which can capture channel state information more comprehensively. In addition, the angular domain compensation can provide a priori information and improve the robustness in noisy environments. The simulation results confirm the feasibility of the proposed ADC-ADMM scheme. © 2023 ACM.

关键词： Millimeter waves

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Large-Scale Riemannian Meta-Optimization via Subspace Adaptation

arXiv

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arXiv 2025年

作者： Yu, Peilin Wu, Yuwei Gao, Zhi Fan, Xiaomeng Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Riemannian meta-optimization provides a promising approach to solving non-linear constrained optimization problems, which trains neural networks as optimizers to perform optimization on Riemannian manifolds. However, existing Riemannian meta-optimization methods take up huge memory footprints in large-scale optimization settings, as the learned optimizer can only adapt gradients of a fixed size and thus cannot be shared across different Riemannian parameters. In this paper, we propose an efficient Riemannian meta-optimization method that significantly reduces the memory burden for large-scale optimization via a subspace adaptation scheme. Our method trains neural networks to individually adapt the row and column subspaces of Riemannian gradients, instead of directly adapting the full gradient matrices in existing Riemannian meta-optimization methods. In this case, our learned optimizer can be shared across Riemannian parameters with different sizes. Our method reduces the model memory consumption by six orders of magnitude when optimizing an orthogonal mainstream deep neural network (e.g. ResNet50). Experiments on multiple Riemannian tasks show that our method can not only reduce the memory consumption but also improve the performance of Riemannian meta-optimization. © 2025, CC BY-NC-ND.

关键词： Constrained optimization

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Accurate and Real-time Hierarchical Ensemble Network for Activity Classification in Construction Worker

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Zuo, Guoyu Wu, Qifei Gao, Wenbin Li, Cheng Sun, Liangkun Yu, Shuangyue Beijing University of Technology School of Information Science and Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

Accurate and real-time locomotion classification is crucial for exoskeletons to assist construction workers in completing multiple tasks. However, state-of-the-art algorithms for classifying multiple activities face multifaceted challenges in both accuracy and real-time capability. In addition, advanced studies typically provide a single solution based on certain sensor combinations, which may have an indirect impact on different assistive devices (e.g., an algorithm using feet IMUs is not suited for bilateral portable hip exoskeletons or unilateral knee exoskeletons), limiting its practicality and applicability in diverse applications. To fill these two gaps, first, we developed a novel hierarchical ensemble network framework that can accurately and real-time classify 11 typical lower limb activities of construction workers. Second, building upon this hierarchical ensemble network framework, we developed 6 configurations wearing IMU sensors on different body segments, which are potentially used for different wearable devices. Experimental results with leave-one-out cross-validation obtained from 10 able-bodied subjects validated the effectiveness of the proposed algorithm. Compared to the baseline ANN-based algorithm, our algorithm under 6 configurations on average was able to improve accuracy, precision, recall, and F1-score by 4.97%, 3.40%, 4.97%, and 5.31%, respectively, and reduce the number of parameters and inference time by 71.86% and 47.85%, respectively. This study showcases multiple solutions with different wearable sensor configurations, offering high accuracy and strong real-time performance for classifying multiple activities, which can be deployed to controllers for multiple types of assistive devices targeting construction workers. © 2013 IEEE.

关键词： Exoskeleton (Robotics)

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Attention Guided 6D Object Pose Estimation with Multi-constraints Voting Network 1

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7th International Conference on Interactive Collaborative Robotics, ICR 2022

作者： Zuo, Guoyu Gu, Zonghan Huang, Gao Gong, Daoxiong Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (数字)9783031236099

ISBN: (纸本)9783031236082

For visual-based robotic manipulation, it has always been a challenging task to perform real-time and accurate pose estimation of target objects under cluttered background, illumination variations, occlusion, and weak texture, especially under severe occlusion conditions. In recent years, the RGB-based methods based on vector field prediction are proved to be robustness on 6D object pose estimation under occlusion. At the same time, network with attention mechanism has achieved outstanding performance in 2D object detection. In this paper, we propose an attention-driven 6D pose estimation method with multi-constraints loss and pixel-wise voting. We calculate the distance weighted unit vector length and included angle length based on prediction results to regularize unit vectors prediction. Moreover, we introduce Dense Atrous Spatial Pyramid Pooling (DenseASPP) and Channel-wise Cross Attention (CCA) mechanisms into the network structure to improve the accuracy of output prediction. Experiments on LINEMOD and Occlusion LINEMOD datasets manifest that our method outperforms state-of-the-art two-stage sparse 2D keypoints prediction methods without pose refinement. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation

Viterbi Decoding of Directed Acyclic Transformer for Non-Aut...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Shao, Chenze Ma, Zhengrui Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. Directed Acyclic Transformer (DA-Transformer) was recently proposed to model sequential dependency with a directed acyclic graph. Consequently, it has to apply a sequential decision process at inference time, which harms the global translation accuracy. In this paper, we present a Viterbi decoding framework for DA-Transformer, which guarantees to find the joint optimal solution for the translation and decoding path under any length constraint. Experimental results demonstrate that our approach consistently improves the performance of DA-Transformer while maintaining a similar decoding speedup. © 2022 Association for Computational Linguistics.

关键词： Decoding

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Sample-Efficient Reinforcement Learning Based on Dynamics Models via Meta-policy Optimization 6th

Sample-Efficient Reinforcement Learning Based on Dynamics Mo...

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6th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2021

作者： Zuo, Guoyu Tian, Zhipeng Huang, Shuai Gong, Daoxiong Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9789811692468

Model-based reinforcement learning (RL) can acquire remarkable sample efficiency, which makes it a suitable choice for applications where experiment data is hard to collect. However, it is difficult to learn an accurate dynamics model fully matched with the real-world, and the accuracy of the model usually affects the agent’s final performance. In this paper, we propose a novel model-based RL approach called Meta-policy Optimization method with branched rollouts (MPOBR), which gets rid of strong dependency on an accurate model. In MPOBR, meta-learning is used to train a policy prior on an ensemble of learned dynamics models, so that this prior can be rapidly adapted to the environment when combined with environment rollouts. To reduce the affect of model compounding bias, short model-generated rollouts branched from real data are used to update the meta-policy. The experiments on simulated robotic tasks are designed to verify the effectiveness of our method. Results show that our approach can achieve the same asymptotic performance of state-of-the-art model-free algorithms while significantly reducing sample complexity. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Optimized Multi-kernel Dictionary Learning and Its Application in Complex Industrial Processes Monitoring 41

Optimized Multi-kernel Dictionary Learning and Its Applicati...

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第41届中国控制会议

作者： Kai Zhong Zhengping Ding Donghui Pan Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information TechnologyAnhui University School of Mathematical Science Anhui University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The data in the chemical process is often complex in structure,and the coupling and correlation between variables are ***,it is difficult for conventional monitoring models to learn the essential characteristics of process data,which greatly affects the performance of the *** at the above problems,this paper proposes an optimized multi-kernel dictionary learning method,which is suitable for the industrial processes with strong nonlinearity and complex data ***,the synthetic kernel fused by multiple kernel functions is used to map the process data to a new high-dimensional feature space to mine the multivariate distribution characteristics of the ***,the grid search method is used to optimize the model parameters in the kernel function for better model ***,the effectiveness and practicability of the proposed method in fault detection are verified on TE benchmark platform.

关键词： Dictionary learning Multi-kernel function Chemical process monitoring

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Adaptive Multi-modal Data Fusion Method for Dermatosis Diagnosis

Adaptive Multi-modal Data Fusion Method for Dermatosis Diagn...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Yaning Yang, Xiaodong Chen, Yiqiang Chen, Shubai Zeng, Bixiao Cai, Hong Chinese Academy of Sciences The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing China University of Jinan College of Information Science and Engineering Shandong China Shandong Academy of Intelligent Computing Technology Shandong China Air Force Medical Center of the PLA Department of Dermatology Beijing China

ISBN: (纸本)9798350337488

Meta-data, e.g., gender, medical history, etc., is of vital importance for dermatosis diagnosis clinically, while most existing AI-based methods ignore this information and only employ dermoscopy images. Due to the modality disequilibrium of image and meta-data, there would be unequal optimization during the modal fusion, resulting in the weak meta-data modality being suppressed and losing their contributions to the classification. To address this issue, we propose an Adaptive Multi-modal Fusion (AMF) method for dermatosis diagnosis, which reweights the fusion factor of image and meta-data by evaluating their optimization discrepancy through the Euclidean norm. What's more, the Co-Attention mechanism is employed to align the two modalities implicitly. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on multi-modal dermatosis public datasets including ISIC 2019, PDA-UFES-20, and HAM10000. The results demonstrate that the proposed method can achieve better performance in comparison to the state-of-the-art methods. © 2023 IEEE.

关键词： Deep Learning Dermatosis Diagnosis Multi-modal Fusion Transformer

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer 29

Improving Fake News Detection of Influential Domain via Doma...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Fake detection

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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