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58th Asilomar Conference on Signals, systems and Computers, ACSSC 2024

作者： Cai, Chang Yuan, Xiaojun Zhang, Ying-Jun Angela The Chinese University of Hong Kong Department of Information Engineering Shatin N.T. Hong Kong Uni. of Electronic Sci. and Tech. of China National Key Lab. of Wireless Commun. Chengdu China

ISBN: (纸本)9798350354058

This paper studies model-driven deep learning tech.iques for precoding design in a task-oriented semantic communication system. In particular, we consider multi-device cooperative edge inference over a wireless multiple-input multiple-output (MIMO) multiple access channel, where multiple devices transmit extracted features to a server to perform a classification task. We formulate the precoding design as an inference accuracy maximization problem. This objective deviates from the traditional focus on throughput, mean-square error (MSE), bit error rate (BER), etc. By leveraging the deep unfolding tech.ique, we develop a precoding network that effectively incorporates the domain knowledge of the solution to the precoding problem. Compared to the optimization based solution, the deep unfolded precoding network exhibits lower complexity by replacing the computation-intensive operations with some learnable parameters. Meanwhile, it offers extra flexibility for end-to-end (E2E) fine-tuning with the semantic codecs to push the performance limit. Simulation results on the ModelNet10 dataset verify that the proposed method achieves significantly higher classification accuracy compared to various baselines. © 2024 IEEE.

关键词： Mean square error

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Multiuser Tracking for Reconfigurable Intelligent Surface Aided MIMO-OFDM systems: A Variational Bayesian Approach 24

Multiuser Tracking for Reconfigurable Intelligent Surface Ai...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Teng, Boyu Yuan, Xiaojun Wang, Rui Uni. of Electronic Sci. and Tech. of China The National Key Lab. on Wireless Commun. Chengdu China Tongji University The College of Electronics and Information Engineering Shanghai China

ISBN: (纸本)9781665496261

In this paper, we study a multiuser tracking problem in the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system aided by multiple reconfigurable intelligent surfaces (RIS). A base station (BS) receives the uplink signals from users reflected by multiple RISs and tracks the positions of these users. Considering the users' mobility and the blockage of light-of-sight (LoS) paths, we establish a probability transition where the geometric constraints between channel parameters and multiuser positions are utilized. An online algorithm is proposed to estimate the multiuser positions, the angles-of-arrivals (AoAs) at RIS, the time delay, and the blockage of the LoS path. Simulation results show that the proposed algorithm can achieve up to centimeter-level tracking accuracy. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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LATEXBLEND: Scaling Multi-concept Customized Generation with Latent Textual Blending

arXiv

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arXiv 2025年

作者： Jin, Jian Yu, Zhenbo Shen, Yang Fu, Zhenyong Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Sci. & Tech. China

Customized text-to-image generation renders user-specified concepts into novel contexts based on textual prompts. Scaling the number of concepts in customized generation meets a broader demand for user creation, whereas existing methods face challenges with generation quality and computational efficiency. In this paper, we propose LATEXBLEND, a novel framework for effectively and efficiently scaling multi-concept customized generation. The core idea of LATEXBLEND is to represent single concepts and blend multiple concepts within a Latent Textual space, which is positioned after the text encoder and a linear projection. LATEXBLEND customizes each concept individually, storing them in a concept bank with a compact representation of latent textual features that captures sufficient concept information to ensure high fidelity. At inference, concepts from the bank can be freely and seamlessly combined in the latent textual space, offering two key merits for multi-concept generation: 1) excellent scalability, and 2) significant reduction of denoising deviation, preserving coherent layouts. Extensive experiments demonstrate that LATEXBLEND can flexibly integrate multiple customized concepts with harmonious structures and high subject fidelity, substantially outperforming baselines in both generation quality and computational efficiency. Project Page. Copyright © 2025, The Authors. All rights reserved.

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Analysis and Optimization of Model Aggregation Performance in Federated Learning

Analysis and Optimization of Model Aggregation Performance i...

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IEEE International Conference on Communications (ICC)

作者： Huiyuan Yang Tian Ding Xiaojun Yuan Yangtze Delta Region Institute University of Electronic Sci. and Tech. of China Huzhou China National Key Lab of Sci. and Tech. on Communications University of Electronic Sci. and Tech. of China Chengdu China Information Engineering The Chinese University of Hong Kong Hong Kong China

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine learning paradigm. One of the main challenges in FL is the huge communication cost for model aggregation. Many compression/quantization schemes have been proposed to reduce the communication cost of model aggregation, with remarkable results. However, the following two questions remain unanswered: What are the performance limits of model aggregation? How much can FL convergence performance be improved by modifying existing model aggregation schemes? In this paper, we manage to answer these two questions. Specifically, for the first question, we put forth a general model aggregation performance analysis framework based on the rate-distortion theory. We then derive an inner bound of the rate-distortion region of model aggregation. For the second question, we develop an algorithm to search for the minimum achievable aggregation distortion, which, combined with the achievability scheme of the derived inner bound, induces a method to approximate the limits of FL convergence performance numerically. Numerical results demonstrate that the baseline model aggregation schemes still have great potential for further improvement.

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CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

CODE-MVP: Learning to Represent Source Code from Multiple Vi...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Wang, Xin Wang, Yasheng Wan, Yao Wang, Jiawei Zhou, Pingyi Li, Li Wu, Hao Liu, Jin School of Computer Science Wuhan University China Huawei Noah's Ark Lab Hong Kong School of Computer Sci. & Tech. Huazhong University of Science and Technology China Faculty of Information Technology Monash University Australia School of Information Science and Engineering Yunnan University China

ISBN: (纸本)9781955917766

Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow Graph). However, most of them only consider a single view of source code independently, ignoring the correspondences among different views. In this paper, we propose to integrate different views with the naturallanguage description of source code into a unified framework with Multi-View contrastive Pre-training, and name our model as CODEMVP. Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework. Inspired by the type checking in compilation, we also design a fine-grained type inference objective in the pre-training. Experiments on three downstream tasks over five datasets demonstrate the superiority of CODE-MVP when compared with several state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms of MRR/MAP/Accuracy metrics on natural language code retrieval, code similarity, and code defect detection tasks, respectively. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Semantics

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Multiuser Tracking for Reconfigurable Intelligent Surface Aided MIMO-OFDM systems: A Variational Bayesian Approach

Multiuser Tracking for Reconfigurable Intelligent Surface Ai...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Boyu Teng Xiaojun Yuan Rui Wang The National Key Lab. on Wireless Commun. Uni. of Electronic Sci. and Tech. of China Chengdu The College of Electronics and Information Engineering Tongji University Shanghai

In this paper, we study a multiuser tracking problem in the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system aided by multiple reconfigurable intelligent surfaces (RIS). A base station (BS) receives the uplink signals from users reflected by multiple RISs and tracks the positions of these users. Considering the users’ mobility and the blockage of light-of-sight (LoS) paths, we establish a probability transition where the geometric constraints between channel parameters and multiuser positions are utilized. An online algorithm is proposed to estimate the multiuser positions, the angles-of-arrivals (AoAs) at RIS, the time delay, and the blockage of the LoS path. Simulation results show that the proposed algorithm can achieve up to centimeter-level tracking accuracy.

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Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance

Driving-Video Dehazing with Non-Aligned Regularization for S...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Junkai Fan Jiangwei Weng Kun Wang Yijun Yang Jianjun Qian Jun Li Jian Yang PCA Lab Nanjing University of Science and Technology China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Sci. & Tech. The Hong Kong University of Science and Technology Guangzhou

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned hazy/clear video pairs for effective model training, especially in dynamic driving scenarios with unpredictable weather conditions. In this paper, we propose a pioneering approach that addresses this challenge through a nonaligned regularization strategy. Our core concept involves identifying clear frames that closely match hazy frames, serving as references to supervise a video dehazing network. Our approach comprises two key components: reference matching and video dehazing. Firstly, we introduce a non-aligned reference frame matching module, leveraging an adaptive sliding window to match high-quality reference frames from clear videos. Video dehazing incorporates flow-guided cosine attention sampler and deformable cosine attention fusion modules to enhance spatial multi-frame alignment and fuse their improved information. To validate our approach, we collect a GoProHazy dataset captured effortlessly with GoPro cameras in diverse rural and urban road environments. Extensive experiments demonstrate the superiority of the proposed method over current state-of-the-art methods in the challenging task of real driving-video dehazing. Project page.

关键词： Training Computer vision Fuses Roads Cameras Safety Pattern recognition

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Task-Oriented Communication for Multi-Device Edge Inference: A Maximal Coding Rate Reduction Approach

Task-Oriented Communication for Multi-Device Edge Inference:...

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IEEE International Conference on Communications (ICC)

作者： Chang Cai Xiaojun Yuan Ying-Jun Angela Zhang Department of Information Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong SAR National Key Lab. of Wireless Commun. Uni. of Electronic Sci. and Tech. of China Chengdu China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we consider a task-oriented communication system for multi-device edge inference over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (feature encoding and classification) and communication (precoding) modules are designed to achieve the same goal of inference accuracy maximization. End-to-end learning of these modules involves both the task dataset and the time-varying wireless channel, which may result in unaffordable training overhead and complexity. Instead, we advocate a modular design of learning and communication to achieve the consistent goal. Specifically, we leverage the maximal coding rate reduction (MCR 2 ) objective as a surrogate to represent the inference accuracy, which allows us to explicitly formulate the precoding optimization problem separated from the learning design. We develop a block coordinate ascent (BCA) algorithm for efficient problem-solving. Moreover, the MCR 2 objective also serves the loss function of the feature encoding network, thus unifying the objectives of learning and communication. Simulation results demonstrate the superior performance of the proposed method compared to various baselines. As such, our work paves the way for further exploration into the synergistic alignment of learning and communication objectives in task-oriented communication systems. Code is available at https://***/chang-cai/TaskCommMCR2.

关键词： Wireless communication Training Accuracy Precoding Simulation Inference algorithms Problem-solving

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Tri-Perspective view Decomposition for Geometry-Aware Depth Completion

Tri-Perspective view Decomposition for Geometry-Aware Depth ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiqiang Yan Yuankai Lin Kun Wang Yupeng Zheng Yufei Wang Zhenyu Zhang Jun Li Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Sci. & Tech. China Huazhong University of Science and Technology Chinese Academy of Sciences Northwestem Poly technical University Nanjing University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Depth completion is a vital taskfor autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. How-ever, most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation, which are still insufficient to capture the fine-grained 3D geometry of the scene. To address this chal-lenge, we introduce Tri-Perspective View Decomposition (TPVD), a novel framework that can explicitly model 3D geometry. In particular, (1) TPVD ingeniously decomposes the original point cloud into three 2D views, one of which corresponds to the sparse depth input. (2) We design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D aggregation, where a Distance-Aware Spherical Convolution (DASC) is applied. (3) By adaptively choosing TPVaffinitive neighbors, the newly proposed Geometric Spatial Propagation Network (GSPN) further improves the geometric consistency. As a result, our TPVD outperforms existing methods on KITTI, NYUv2, and SUN RGBD. Fur-thermore, we build a novel depth completion dataset named TOFDC, which is acquired by the time-of-flight (TOF) sen-sor and the color camera on smart phones. Project page.

关键词： Geometry Point cloud compression Solid modeling Three-dimensional displays Laser radar Convolution Pattern recognition

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Deep Unfolding Based Precoding for Task-Oriented Semantic Communications Over MIMO Channels

Deep Unfolding Based Precoding for Task-Oriented Semantic Co...

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Asilomar Conference on Signals, systems & Computers

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

关键词： Performance evaluation Accuracy Precoding Simulation Bit error rate Semantic communication Feature extraction Throughput Servers Optimization

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