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Automatic adaptation of a face model using action units for semantic coding of videophone sequences

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1998年第6期8卷 781-795页

作者： Zhang, L Univ Hannover Inst Theoret Nachrichtentech & Informat Verarbeit D-30167 Hannover Germany

The topic of investigation is automatic adaptation of a face model at the beginning of a videophone sequence for implementing mimic analysis by means of action units in a semantic coder. Here, not only the face model is to be adapted to match the real face, but also initial values of action units are to be determined. In the proposed algorithm, eye and mouth features are first estimated using deformable templates. Then, the face model Candide is adapted to these estimated features in three steps, namely: 1) the global adaptation;2) the local adaptation;and 3) the mimic adaptation, For the mimic adaptation, six action units are used and their initial values are determined. The proposed adaptation algorithm differs from previous works in the following aspects: 1) there is no restriction on the rotation for the global adaptation of the face model and 2) initial values of action units are determined due to the mimic adaptation. The proposed algorithm has been experimented onto synthetic images and natural head-and-shoulder videophone sequences with a spatial resolution corresponding to CIF and a frame rate of 10 Hz, The average errors for the estimation of eye and mouth features and for the adaptation of the face model amount to 1.936 (pel) and 2.009 (pel), respectively. With this adaptation algorithm, mimic analysis for semantic coding by means of action units in the subsequent frames is realizable.

关键词： action units adaptation face model semantic coding

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Reconstruction of Radar Pulse Repetition Pattern via semantic coding of Intercepted Pulse Trains

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第1期59卷 394-403页

作者： Yuan, Shuo Kang, Shi-Qian Shang, Wen-Xiu Liu, Zhang-Meng Natl Univ Def Technol State Key Lab Complex Electromagnet Environm Effec Changsha 410073 Peoples R China Tianjin Inst Adv Technol Tianjin Peoples R China

The timing structure of multiple successive radar pulses constructs a high-dimensional pattern for intercepted pulse trains, which is called as pulse repetition interval (PRI) pattern. By treating the radar as a machine that uses differently permuted pulses to communicate with surroundings, this pattern acts as the grammatical structure of its language. Compared with the discrete PRI set that is conventionally used for pulse train description, PRI pattern contains richer and more condensed structural information about radar pulse train, which is helpful for pulse deinterleaving and radar recognition. This article introduces the semantic coding theory to reveal and reconstruct PRI pattern from the intercepted radar pulse train. We first define the coding complexity of a pulse train, which is divided into two parts: 1) the complexity of encoding a PRI pattern dictionary with each element consisting of several successive PRIs;and 2) encoding the intercepted pulse train based on this dictionary. The coding complexity is then minimized by optimizing the components in dictionary, and the PRI timing patterns are finally obtained from the dictionary when the minimization is reached. The effectiveness of semantic coding model and PRI pattern reconstruction method is verified in the simulation part.

关键词： Encoding Radar Dictionaries Modulation semantics Complexity theory Timing Compression coding pulse repetition interval (PRI) pattern reconstruction radar signal processing semantic coding

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Ambiguity and relatedness effects in semantic tasks: Are they due to semantic coding?

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JOURNAL OF MEMORY AND LANGUAGE 2006年第2期55卷 247-273页

作者： Hino, Yasushi Pexman, Penny M. Lupker, Stephen J. Waseda Univ Fac Arts & Sci Shinjuku Ku Tokyo 1628644 Japan Univ Calgary Dept Psychol Calgary AB T2N 1N4 Canada Univ Western Ontario Dept Psychol London ON N6A 5C2 Canada

According to parallel distributed processing (PDP) models of visual word recognition, the speed of semantic coding is modulated by the nature of the orthographic-to-semantic mappings. Consistent with this idea, an ambiguity disadvantage and a relatedness-of-meaning (ROM) advantage have been reported in some word recognition tasks in which semantic processing is presumed to be required for responding. To further evaluate this idea, we examined ambiguity and ROM effects in lexical decision and semantic categorization tasks. In the lexical decision task, there was an ambiguity advantage but no ROM effect. In the semantic categorization tasks, we used various semantic categories and found a processing time disadvantage for ambiguous words with less related meanings when the decisions were relatively difficult, but observed no effect when the decisions were easier. These results suggest that both the ambiguity disadvantage and the ROM advantage in the semantic categorization tasks are due to decision-making, rather than semantic-coding, processes. The implications of these results for issues concerning the nature of semantic coding and semantic representations are discussed. (c) 2006 Elsevier Inc. All rights reserved.

关键词： ambiguity effect relatedness-of-meanings effect semantic coding words' orthographic-to-semantic relationships

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Role of anterior cingulate cortex during semantic coding in verbal working memory

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NEUROSCIENCE LETTERS 2008年第1期436卷 57-61页

作者： Kaneda, Mizuki Osaka, Naoyuki Kyoto Univ Grad Sch Letters Dept Psychol Sakyo Ku Kyoto 6068501 Japan

The relationship between working memory and long-term memory was one of the main problems in recent studies of working memory. As part of this problem, we examined the neural substrates that sustain the semantic coding process in verbal working memory using fMRI. In past experiments, we behaviorally explored whether central executive plays an important role in the process of semantic coding;thus, we especially focused on the function of anterior cingulate cortex (ACC) that is assumed to form the main neural basis of central executive. We accomplished Our purpose by examining the concreteness effect reflecting semantic information. The ACC was strongly activated under the Concrete condition in contrast to the Abstract condition. Based on this result, we argue that the ACC is responsible for the semantic coding process in verbal working memory. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

关键词： verbal working memory semantic coding central executive anterior cingulate cortex concreteness effect fMRI

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Neural Network Approach for semantic coding of Words 15th

Neural Network Approach for Semantic Coding of Words

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15th International Scientific Conference on Intellectual Systems of Decision Making and Problems of Computational Intelligence (ISDMCI)

作者： Golovko, Vladimir Kroshchanka, Alexander Komar, Myroslav Sachenko, Anatoliy Brest State Tech Univ Brest BELARUS Panstwowa Szkola Wyzsza Papieza Jana Pawla II Biala Podlaska Poland Ternopil Natl Econ Univ Ternopol Ukraine Kazimierz Pulaski Univ Technol & Humanities Radom Poland

ISBN: (纸本)9783030264741

This article examines and analyzes the use of the word2vec method for solving semantic coding problems. The task of semantic coding has acquired particular importance with the development of search system. The relevance of such technologies is associated primarily with the ability to search in large-volume databases. Based on the obtained practical results, a prototype of a search system based on the use of selected semantic information for the implementation of relevant search in the database of documents has been developed. Proposed two main scenarios for the implementation of such a search. The training set has been prepared on the basis of documents in the English version of Wikipedia, which includes more than 100,000 original articles. The resulting set was used in the experimental part of the work to test the effectiveness of the developed prototype search system.

关键词： Neural network semantic coding Search system Training set Word2vec method

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Deep semantic coding for Wireless Image Retrieval 96

Deep Semantic Coding for Wireless Image Retrieval

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IEEE 96th Vehicular Technology Conference (VTC-Fall)

作者： Wang, Ying Qi, Chenhao Southeast Univ Natl Mobile Commun Res Lab Sch Informat Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781665454681

We address the image retrieval problem for a wireless system including an edge server and an edge device. The query image is first compressed by the edge device, and then transmitted into wireless channel, while the edge server retrieves the received image. Different from conventional schemes directly compressing features via unsupervised learning regardless of the database semantic distribution, we design a deep semantic coding (DSC) scheme by integrating the inverted semantic index structure of the database into the coding process, which can utilize the prior semantic information of the database to reduce the bandwidth. We extract the feature vectors from the images via a convolutional neural network and generate the semantic guided code head, which is followed by the product quantization. The experimental results verify the effectiveness of the DSC scheme in reducing the bandwidth as well as improving the performance of wireless image retrieval.

关键词： Convolutional neural network deep learning semantic coding wireless image retrieval

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MINIMALLY-SUPERVISED SPEECH SYNTHESIS WITH CONDITIONAL DIFFUSION MODEL AND LANGUAGE MODEL: A COMPARATIVE STUDY OF semantic coding 49

MINIMALLY-SUPERVISED SPEECH SYNTHESIS WITH CONDITIONAL DIFFU...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiane, Chunyu Li, Hao Ni, Hao Qu, He Fu, Ruibo Wang, Tao Wang, Longbiao Dang, Jianwu Tianjin Univ Sch New Media & Commun Tianjin Peoples R China Kuaishou Technol Co Ltd Beijing Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Recently, there has been a growing interest in text-to-speech (TTS) methods that can be trained with minimal supervision by combining two types of discrete speech representations and using two sequence-to-sequence tasks to decouple TTS. However, existing methods suffer from three problems: the high-frequency waveform distortion of discrete speech representations, the prosodic averaging problem caused by the duration prediction model in non-autoregressive frameworks, and difficulty in prediction due to the information redundancy and dimension explosion of existing semantic coding methods. To address these problems, three progressive methods are proposed. First, we propose Diff-LM-Speech, an autoregressive structure consisting of a language model and diffusion models, which models the semantic embedding into the mel-spectrogram based on a diffusion model to achieve higher audio quality. We also introduce a prompt encoder structure based on a variational autoencoder and a prosody bottleneck to improve prompt representation ability. Second, we propose Tetra-Diff-Speech, a non-autoregressive structure consisting of four diffusion model-based modules that design a duration diffusion model to achieve diverse prosodic expressions. Finally, we propose Tri-Diff-Speech, a non-autoregressive structure consisting of three diffusion model-based modules that verify the non-necessity of existing semantic coding models and achieve the best results. Experimental results show that our proposed methods outperform baseline methods. We provide a website with audio samples. (1)

关键词： minimal supervision speech synthesis semantic coding diffusion model language model

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Object-attribute-relation model-based semantic coding for image transmission

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第11期361卷

作者： Li, Chenxing Duan, Yiping Tao, Xiaoming Hu, Shuzhan Yang, Qianqian Chen, Changwen Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310058 Zhejiang Peoples R China Hong Kong Polytech Univ Hong Kong 100872 Peoples R China

The development of digital, networked and intelligent communication technology has led to significant growth in data and services. Especially in the communication scenario of monitoring data, a variety of downstream tasks bring significant technical challenges for highperformance data transmission, information mining, and data distribution. The problem to be solved is the coding and low bit -rate transmission of large amounts of surveillance data. Concepts and methods that utilize semantic communication are needed. This paper proposes a communication architecture based on the object-attribute-relation (OAR) semantic knowledge base. The sender uses the model parameters and prior knowledge in the knowledge base to drive the OAR extraction model to encode the image. After semantic coding and channel coding, the signal is transmitted to the receiver through the physical channel. At the receiver, the signal is decoded into a semantic vector. The OAR global vector is generated by structured processing, which is used for downstream tasks such as image retrieval or image reconstruction. Experimental comparison and analysis illustrate the effectiveness and superiority of each part of the communication framework. This approach provides a new solution for improving data utilization and processing efficiency.

关键词： semantic knowledge base Object-attribute-relation (OAR) Image retrieval semantic coding semantic communication

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semantic-Aware Jointed coding and Routing Design in Large-Scale Satellite Networks: A Deep Learning Approach

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第6期32卷 5415-5429页

作者： Gao, Ronghao Xu, Yunlai Li, Han Zhang, Qinyu Yang, Zhihua Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

In large-scale satellite networks, data delivery confronts obvious challenges such as high loss rate and long propagation delay leading to low Packet Delivery Ratio (PDR) and huge delivery latency over intermittent Inter-Satellite Links (ISLs), making the current routing algorithms exploiting typical Automatic Repeat reQuest (ARQ) mechanisms extremely inefficient and even incapable. To address this issue, in this paper, we propose a semantic-aware coding and routing joint mechanism called semantic Adaptive coding and Routing (SACR) by considering both the semantic correlations in the context-dependent data and the link status knowledge. In particular, the proposed SACR achieves excellent error-tolerant and routing-agile capabilities by an elaborately interactive design consisting of a customized routing-aware semantic Adaptive coding Hybrid ARQ (SAC-HARQ) mechanism and a semantic coding-based Routing Mechanism (SCRM). The simulation results indicate that the proposed SACR mechanism performs better in reducing the average delivery latency and improving the effective throughput compared with typical routing mechanisms such as Open Shortest Path First (OSPF) routing, Deep Q-Networks based Intelligent Routing (DQN-IR), and Real-Time Hop-by-hop routing (RTHop), integrating with typical semantic coding methods, i.e., Deep Learning-based Joint Channel-Source coding (DL-JSCC), Deep learning-based semantic Communication system (DeepSC), and semantic coding HARQ (SCHARQ), respectively.

关键词： Satellite network semantic coding routing deep reinforcement learning

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MDVSCEfficient Wireless Model Division Video semantic Communication

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IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 1109-1124页

作者： Bao, Zhicheng Liang, Haotai Dong, Chen Li, Cong Xu, Xiaodong Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518000 Peoples R China Acad Space Elect Informat Technol Natl Key Lab Sci & Technol Space Microwave Xian 710100 Peoples R China

This article introduces a novel method for transmitting video data over noisy wireless channels with high efficiency and controllability. The method derivates from model division multiple access (MDMA) to extract common semantic features from video frames. It also uses deep joint source-channel coding (JSCC) as the main framework to establish communication links and deal with channel noise. An entropy-based semantic importance coding scheme is developed to adjust the data amount accurately and explicitly. We name our method as model division video semantic communication (MDVSC). The main steps of our approach are as follows: first, video frames are transformed into a latent space to reduce computational complexity and redistribute data. Then, common features and individual features are extracted, and semantic importance coding is applied to further eliminate redundant semantic information under the communication bandwidth constraint. We evaluate our method on standard video test sequences and compare it with traditional wireless video coding methods. The results show that MDVSC generally surpasses the conventional methods in terms of quality metrics and has the capability to control code length precisely. Moreover, additional experiments and ablation studies are conducted to demonstrate its potential for various tasks.

关键词： semantics Encoding Feature extraction Wireless communication Channel coding Streaming media Bandwidth Joint source-channel coding (JSCC) latent transform model division multiple access (MDMA) semantic coding semantic communications video transmission

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