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IEEE International Symposium on Information Theory

作者： Yijun Fan Yanxiao Liu Yi Chen Shenghao Yang Raymond W. Yeung Department of Information Engineering The Chinese University of Hong Kong School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

We consider a generalized collision channel model for general multi-user communication systems, an extension of Massey and Mathys’ collision channel without feedback for multiple access communications. In our model, there are multiple transmitters and receivers sharing the same communication channel. The transmitters are not synchronized and arbitrary time offsets between transmitters and receivers are assumed. A "collision" occurs if two or more packets from different transmitters partially or completely overlap at a receiver. Our model includes the original collision channel as a special *** paper focuses on reliable throughputs that are approachable for arbitrary time offsets. We consider both slot-synchronized and non-synchronized cases and characterize their reliable throughput regions for the generalized collision channel model. These two regions are proven to coincide. Moreover, it is shown that the protocol sequences constructed for multiple access communication remain "throughput optimal" in the generalized collision channel model. We also identify the protocol sequences that can approach the outer boundary of the reliable throughput region.

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding

Tracking Objects and Activities with Attention for Temporal ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zeyu Xiong Daizong Liu Pan Zhou Jiahao Zhu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wangxuan Institute of Computer Technology Peking University

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets’ behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed.

关键词： Solid modeling Target tracking Three-dimensional displays Grounding Benchmark testing Signal processing Feature extraction

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FedRC: A Rapid-Converged Hierarchical Federated Learning Framework in Street Scene Semantic Understanding

FedRC: A Rapid-Converged Hierarchical Federated Learning Fra...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Wei-Bin Kou Qingfeng Lin Ming Tang Shuai Wang Guangxu Zhu Yik-Chung Wu Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China Shenzhen Research Institute of Big Data Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model faces poor generalization issue due to inter-city domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization, but suffers from slow convergence rate because of vehicles’ surrounding heterogeneity across cities. Going beyond existing HFL works that have deficient capabilities in complex tasks, we propose a rapid-converged heterogeneous HFL framework (FedRC) to address the inter-city data heterogeneity and accelerate HFL model convergence rate. In our proposed FedRC framework, both single RGB image and RGB dataset are modelled as Gaussian distributions in HFL aggregation weight design. This approach not only differentiates each RGB sample instead of typically equalizing them, but also considers both data volume and statistical properties rather than simply taking data quantity into consideration. Extensive experiments on the TriSU task using across-city datasets demonstrate that FedRC converges faster than the state-of-the-art benchmark by 38.7%, 37.5%, 35.5%, and 40.6% in terms of mIoU, mPrecision, mRecall, and mF1, respectively. Furthermore, qualitative evaluations in the CARLA simulation environment confirm that the proposed FedRC framework delivers top-tier performance.

关键词： Federated learning Semantics Urban areas Gaussian distribution Benchmark testing data models intelligent robots Faces Autonomous vehicles Convergence

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Voice Conversion Augmentation for Speaker Recognition on Defective datasets

arXiv

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arXiv 2024年

作者： Tao, Ruijie Shi, Zhan Jiang, Yidi Liu, Tianchi Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore School of Data Science The Chinese University of Hong Kong Shenzhen518172 China Singapore Institute of Technology Institute for Infocomm Research A∗STAR Singapore138632 Singapore Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China University of Bremen Bremen28359 Germany

Modern speaker recognition system relies on abundant and balanced datasets for classification training. However, diverse defective datasets, such as partially-labelled, small-scale, and imbalanced datasets, are common in real-world applications. Previous works usually studied specific solutions for each scenario from the algorithm perspective. However, the root cause of these problems lies in dataset imperfections. To address these challenges with a unified solution, we propose the Voice Conversion Augmentation (VCA) strategy to obtain pseudo speech from the training set. Furthermore, to guarantee generation quality, we designed the VCA-NN (nearest neighbours) strategy to select source speech from utterances that are close to the target speech in the representation space. Our experimental results on three created datasets demonstrated that VCA-NN effectively mitigates these dataset problems, which provides a new direction for handling the speaker recognition problems from the data aspect. © 2024, CC BY.

关键词： Classification (of information)

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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Infer the Whole from a Glimpse of a Part: Keypoint-Based Knowledge Graph for Vehicle Re-Identification 39

Infer the Whole from a Glimpse of a Part: Keypoint-Based Kno...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lv, Kai Li, Yunlong Chen, Zhuo Wang, Shuo Han, Sheng Lin, Youfang School of Computer Science & Technology Beijing Jiaotong University China Beijing Key Laboratory of Traffic Data Mining Embodied Intelligence China Zhejiang University China CSSC Intelligent Innovation Research Institute China CSSC Systems Engineering Research Institute China

ISBN: (纸本)157735897X

Vehicle re-identification aims to match vehicles across non-overlapping camera views. Many existing methods extract features from one specific image, and these methods lack view-invariance when comparing vehicles of different orientations. As a result, discriminative parts obscured by viewpoint changes cannot contribute effectively to matching. This work presents a novel keypoint-based framework for vehicle Re-ID. We propose to explicitly model the intrinsic structural relationships between vehicle components via knowledge graph. By establishing connection between keypoints, our approach aims to leverage such prior to match vehicles even when some parts are not directly comparable due to orientation inconsistencies. Specifically, given query and gallery images, we first detect visible keypoints. Then, a transformer-based model infers features for non-overlapped keypoints by conditioning on visible correspondences defined in the knowledge graph. The final representation integrates visible and inferred features. Extensive experiments demonstrate our method outperforms state-of-the-arts on standard benchmarks under cross-view matching scenarios. To our knowledge, this is the first work introducing structural priors via keypoint knowledge graphs for view-invariant vehicle re-identification. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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A dual-network micro-expression recognition model based on optical flow features

A dual-network micro-expression recognition model based on o...

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International Conference on big data Computing and Communications (bigCOM)

作者： Xiaofang Mu Jiaji Liu Yue Li Hui Qi Zhenyu Liu Hao Li Department of Computer Science Taiyuan Normal University Taiyuan China Department of Big Data and Intelligent Engineering Shanxi Institute of Technology Yangquan China

Human micro-expressions are difficult to detect, and their duration is approximately 1/25 to 1/5 s. Recognition and research on micro-expression can help determine the real psy-chological state of human beings, and the application scenarios are very wide. In this research, in addition to computer vision knowledge, a deep convolutional neural network for detecting facial key points and a deep learning method for extracting optical flow features were combined to construct a dual-network micro-expression recognition model based on optical flow features. The facial region of interest was divided in accordance with the key points of the face, and the optical flow features in the regions of interest were statistically analyzed. Because the original optical flow features contained in the micro-expression video clips have many dimension and high redundancy, an improved optical flow direction histogram was adopted. The histogram of oriented optical flow removed redundant features and enhanced the ability of optical flow features to describe micro-expression. Finally, the classification algorithm of the support vector machine was used to realize micro-expression recognition. The experimental results on the video-based facial micro-macro-expression database MMEW showed that the dual-network model constructed here improved the accuracy of micro-expression recognition and better completed the task of micro-expression recognition.

关键词： Support vector machines Histograms Face recognition Computational modeling Redundancy Feature extraction data models

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MacST: Multi-Accent Speech Synthesis via Text Transliteration for Accent Conversion

arXiv

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arXiv 2024年

作者： Inoue, Sho Wang, Shuai Wang, Wanxing Zhu, Pengcheng Bi, Mengxiao Li, Haizhou School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong CUHK-Shenzhen Shenzhen China Fuxi AI Lab NetEase Inc. Hangzhou China Department of Electrical and Computer Engineering National University of Singapore Singapore

In accented voice conversion or accent conversion, we seek to convert the accent in speech from one another while preserving speaker identity and semantic content. In this study, we formulate a novel method for creating multi-accented speech samples, thus pairs of accented speech samples by the same speaker, through text transliteration for training accent conversion systems. We begin by generating transliterated text with Large Language Models (LLMs), which is then fed into multilingual TTS models to synthesize accented English speech. As a reference system, we built a sequence-to-sequence model on the synthetic parallel corpus for accent conversion. We validated the proposed method for both native and non-native English speakers. Subjective and objective evaluations further validate our dataset’s effectiveness in accent conversion studies. © 2024, CC BY.

关键词： Semantics

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A Linearly Convergent Robust Compressed Push-Pull Method for Decentralized Optimization

A Linearly Convergent Robust Compressed Push-Pull Method for...

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IEEE Conference on Decision and Control

作者： Yiwei Liao Zhuorui Li Shi Pu School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China H. Milton Stewart School of Industrial and System Engineering Georgia Institute of Technology Atlanta USA

In the modern paradigm of multi-agent networks, communication has become one of the main bottlenecks for decentralized optimization, where a large number of agents are involved in minimizing the average of the local cost functions. In this paper, we propose a robust compressed push-pull algorithm (RCPP) that combines gradient tracking with communication compression. In particular, RCPP is compatible with a much more general class of compression operators that allow both relative and absolute compression errors. We show that RCPP achieves linear convergence rate for smooth objective functions satisfying the Polyak-Lojasiewicz condition over general directed networks. Numerical examples verify the theoretical findings and demonstrate the efficiency, flexibility, and robustness of the proposed algorithm.

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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

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science China(Information sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph \({\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}\) model communication complexity round complexity

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