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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Liu, Xiangbin Du, Junping Liang, Meiyu Li, Ang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science National Pilot Software Engineering School Beijing100876 China

ISBN: (纸本)9781665477352

technology videos contain rich multi-modal information. In cross-modal information search, the data features of different modalities cannot be compared directly, so the semantic gap between different modalities is a key problem that needs to be solved. To address the above problems, this paper proposes a novel Feature Fusion based Adversarial Cross-modal Retrieval method (FFACR) to achieve text-to-video matching, ranking and searching. The proposed method uses the framework of adversarial learning to construct a video multimodal feature fusion network and a feature mapping network as generator, a modality discrimination network as discriminator. Generator and discriminator are trained alternately based on adversarial learning, so that the data obtained by the feature mapping network is semantically consistent with the original data and the modal features are eliminated. Experimental results demonstrate that the proposed method performs better in text-to-video search on the self-built datasets of technology videos. © 2022 IEEE.

关键词： Semantics

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A Hessian-Laplace Based Blob Detector for Stamp Image Classification

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Chinese Journal of Electronics 2023年第4期19卷 671-675页

作者： Anlong Ming Huadong Ma Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Beijing University of Posts and Telecommunications Beijing China

Blob features are usually used as texture descriptors for object classification and most traditional blob detectors nowadays are luminance-based. However, in many applications we could require a blob detector for the color domain. In this paper, we propose a novel and efficient framework for the extension from scalar-signals to vector-signals of multi-scale blob detection to prevent information loss due to gray scale transformation. Then, we present a blob detector for stamp image classification to distinguish the stamp types. In our experiments, we compare our method to other approaches. The experimental results demonstrate the effectiveness of our proposed detector.

关键词： Image color analysis Detectors Feature extraction Distortion Research and development Blob detection Image classification

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Multi-View User Preference Modeling for Personalized Text-to-Image Generation

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IEEE Transactions on multimedia 2025年 27卷 3082-3091页

作者： Zhang, Huaiwen Wu, Tianci Wei, Yinwei Inner Mongolia University College of Computer Science Hohhot010021 China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot010021 China Shandong University School of Software Jinan250100 China

Personalized text-to-image generation aims to synthesize images tailored to individual user preferences. Existing methods primarily generate customized content using a few reference images, which often struggle to mine user preferences from historical records, and thus fail to synthesize truly personalized content. In addition, it is difficult to directly incorporate the extracted feature of user preferences into the feature space of the generation model, since there exists a considerable gap between them. In this paper, we propose a novel multi-view personalized text-to-image generation method based on the diffusion model, named MVP-Diffusion, which learns instance- and user-level preferences from historical records and integrates them into the generation model. For instance-level user preference modeling, we employ a chain-of-thought prompting strategy to deduce preference keywords and integrate them into input prompts with the aid of a large language model. For user-level preference modeling, we construct a learnable embedding for each user to capture more comprehensive preferences by analyzing their historical records. An adaptive user preference fusion module is proposed to inject user preferences into the generation model via a set of learnable parameters. Experimental results demonstrate that the proposed method significantly enhances the personalization of the generated images compared to the other personalized text-to-image generation methods. © 1999-2012 IEEE.

关键词： Adaptation models User preference Text to image Large language models Training Analytical models Image synthesis Feature extraction Diffusion models Symbols

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Applying HOSVD to Alleviate the Sparsity Problem in Context-aware Recommender Systems

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Chinese Journal of Electronics 2023年第4期22卷 773-778页

作者： WANG Licai MENG Xiangwu ZHANG Yujie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing China

It is quite a great challenge for Context-aware recommender systems (CARS) to generate accurate recommendations with only a few available none-zero contextual user preferences. This paper presents a new approach to alleviate this high sparsity problem by applying the Higher order singular value decomposition (HOSVD) technique. Firstly, it constructs an N-order tensor to represent multidimensional contextual user preferences and decompose it into (N - 2) 3-order tensors according to different types of context (such as time, location and activity). Secondly, it introduces HOSVD to automatically discover the latent associations among these different 3-dimensional objects and predicts unknown unidimensional contextual user preferences. Finally, it calculates every contextual influence coefficient that each type of context factor influences user preferences and then constructs a new N-order tensor using weighted linearization method in order to provide recommendations. Experimental evaluation on a simulated personalized mobile services environment demonstrates the efficacy of our approach against the other baseline ones.

关键词： Tensors Education Automobiles Recommender systems Singular value decomposition

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G3R: Generating Rich and Fine-grained mmWave Radar Data from 2D Videos for Generalized Gesture Recognition

arXiv

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arXiv 2024年

作者： Deng, Kaikai Zhao, Dong Zheng, Wenxin Ling, Yue Yin, Kangwen Ma, Huadong Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China

Millimeter wave radar is gaining traction recently as a promising modality for enabling pervasive and privacy-preserving gesture recognition. However, the lack of rich and fine-grained radar datasets hinders progress in developing generalized deep learning models for gesture recognition across various user postures (e.g., standing, sitting), positions, and scenes. To remedy this, we resort to designing a software pipeline that exploits wealthy 2D videos to generate realistic radar data, but it needs to address the challenge of simulating diversified and fine-grained reflection properties of user gestures. To this end, we design G3R with three key components: (i) a gesture reflection point generator expands the arm’s skeleton points to form human reflection points;(ii) a signal simulation model simulates the multipath reflection and attenuation of radar signals to output the human intensity map;(iii) an encoder-decoder model combines a sampling module and a fitting module to address the differences in number and distribution of points between generated and real-world radar data for generating realistic radar data. We implement and evaluate G3R using 2D videos from public data sources and self-collected real-world radar data, demonstrating its superiority over other state-of-the-art approaches for gesture recognition. Copyright © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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Multimodal Knowledge Graph-Guided Cross-Modal Graph Network for Image-Text Retrieval

Multimodal Knowledge Graph-Guided Cross-Modal Graph Network ...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Juncheng Zheng Meiyu Liang Yang Yu Junping Du Zhe Xue Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

Image-text retrieval is a fundamental cross-modal task, which dedicates to align the representation space between image modality and text modality. Existing cross-interactive image-text retrieval methods generate image and sentence em-beddings independently, introduce interaction-based networks for cross-modal reasoning, and then retrieve them using matching metrics. However, existing approaches do not consider fully utilizing semantic relationships among multimodal knowledge to enhance cross-modal fine-grained implicit semantic reasoning capabilities. In this paper, we propose Multimodal Knowledge Graph-guided Cross-modal Graph Network (MKCGN) that exploits multimodal knowledge graphs to explore cross-modal relationships and enhance global representations. In MKCGN, images generate semantic and spatial graphs, which are used to represent visual graphs, and sentences generate textual graphs based on word semantic relations. The visual and textual graphs are used to implement inter-modal reasoning respectively. Then we obtain interest embeddings of image regions and text words based on entity embed dings in Multimodal Knowledge Graph (MKG), which approximates and aligns the representation space of regions and words to a certain extent, thus obtaining effective inter-modal interactions and learning fine-grained cross-modal communication through graph node contrast loss for inter-modal semantic reasoning. Finally, we mine the implicit semantics and potential relationships of images and texts through the MKG as a means of enhancing the global representations and use cross-modal contrast loss to narrow the space of coarse-grained cross-modal representations. Experiments on the MS-COCO and Flickr30K benchmark datasets show that our proposed MKCGN outperforms state-of-the-art image-text retrieval methods.

关键词：

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Unsupervised Multimodal Graph Contrastive Semantic Anchor Space Dynamic Knowledge Distillation Network for Cross-Media Hash Retrieval

Unsupervised Multimodal Graph Contrastive Semantic Anchor Sp...

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International Conference on Data Engineering

作者： Yang Yu Meiyu Liang Mengran Yin Kangkang Lu Junping Du Zhe Xue Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Cross-media hash retrieval are efficient and effective techniques for retrieval on multi-media database. The success of the Multimodal Large Models (MLM) provides a valuable direction to enhance the accuracy of multimodal hash retrieval, which achieves decent retrieval accuracy with finetuning the pretrained multimodal large models, but their massive model parameters significantly reduce retrieval efficiency. Knowledge Distillation (KD) methods enable small models to learn from the knowledge of larger models, achieving a reduction in model parameter count while ensuring a certain level of accuracy. However, current KD methods face challenges when applied in the multimodal domain, as it requires preserving the multimodal semantic information while minimizing accuracy degradation. To address these challenges, we propose a novel unsupervised multimodal graph contrastive semantic anchor space dynamic knowledge distillation network for cross-media hash retrieval (GASKN). Firstly, to obtain a multimodal semantic anchor space, we construct a large multimodal fusion teacher model using the BEiT-3 model as the backbone. This teacher model is capable of encoding data from different modalities, such as images and text, using the same multimodal encoder to acquire multimodal hash codes that contain rich information from both modalities simultaneously. Secondly, to ensure efficient retrieval capabilities for the student model, we utilize the ALBERT text encoding model and the BiFormer image encoding model as the compact student model's backbones. This allows us to build a lightweight student model with only a twentieth of the parameter count of the teacher model. We propose a dynamic knowledge distillation technique to transfer the multimodal semantic anchor space knowledge embedded in the multimodal large teacher model to the lightweight student model as much as possible. Thirdly, to further distill the structural knowledge of the semantic anchor space from the teacher model to

关键词： Knowledge engineering Degradation Accuracy Image coding Databases Heuristic algorithms Semantics

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QianXun: A Novel Enterprise File Search Framework 25

QianXun: A Novel Enterprise File Search Framework

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Si, Chun Qi, Peng Xue, Hongjia Sun, Yan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing100876 China University of Nottingham Computer Science Faculty of Science and Engineering United Kingdom

ISBN: (纸本)9781665405270

With the deepening of the digital transformation, enterprises have accumulated plenty of electronic files. For users, how to quickly find their desired files from the massive resources is a big challenge, which is also a critical concern for enterprises. Although a series of enterprise search engines have emerged in the market, there is a lack of search systems for enterprise files. In this paper, we design a novel enterprise file search framework, named QianXun, which supports searching in both Chinese and English. By introducing technologies such as automatic text summarization, online file previewing, and Knowledge Graph-enhanced search, our proposed framework improves the efficiency of file search as well as the user experience, which is of great significance to enterprises. © 2022 IEEE.

关键词： Knowledge graph

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Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation

arXiv

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arXiv 2024年

作者： Zhao, Zhe Qi, Mengshi Ma, Huadong State Key Laboratory of Networking and Switching Technology China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia China Beijing University of Posts and Telecommunications China

Generating realistic human grasps is a crucial yet challenging task for applications involving object manipulation in computer graphics and robotics. Existing methods often struggle with generating fine-grained realistic human grasps that ensure all fingers effectively interact with objects, as they focus on encoding hand with the whole representation and then estimating both hand posture and position in a single step. In this paper, we propose a novel Decomposed Vector-Quantized Variational Autoencoder (DVQ-VAE) to address this limitation by decomposing hand into several distinct parts and encoding them separately. This part-aware decomposed architecture facilitates more precise management of the interaction between each component of hand and object, enhancing the overall reality of generated human grasps. Furthermore, we design a newly dual-stage decoding strategy, by first determining the type of grasping under skeletal physical constraints, and then identifying the location of the grasp, which can greatly improve the verisimilitude as well as adaptability of the model to unseen hand-object interaction. In experiments, our model achieved about 14.1% relative improvement in the quality index compared to the state-of-the-art methods in four widely-adopted benchmarks. Our source code is available at https://***/florasion/D-VQVAE. © 2024, CC BY.

关键词： computer graphics

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Hierarchical Knowledge Representation of Complex Tasks Based on Dynamic Motion Primitives 7th

Hierarchical Knowledge Representation of Complex Tasks Based...

引用

7th International Conference on Cognitive Systems and Information Processing, ICCSIP 2022

作者： Miao, Shengyi Zhong, Daming Miao, Runqing Sun, Fuchun Wen, Zhenkun Huang, Haiming Zhang, Xiaodong Wang, Na College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Modern Post Beijing University of Posts and Telecommunications Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100083 China College of Computer Science and Software Shenzhen University Shenzhen518060 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Application Beijing100083 China

ISBN: (纸本)9789819906161

Current service robots without learning ability are not qualified for many complex tasks. Therefore, it is very significant to decompose the complex task into repeatable execution unit. In this paper, we propose a complex task representation method based on dynamic motion primitives, and use hierarchical knowledge graph to represent the analytic results of complex tasks. To realize the execution of complex robot manipulation tasks, we decompose the semantic tasks into the minimum motion units that can be executed by the robot and combine the multi-modal information: posture, force and robot joint parameters, obtained by the sensors. We use the knowledge graph to record the end-effector required by the robot to perform different tasks and make appropriate selection of end-effector according to different needs. Finally, Taking the long sequence complex task of service scene as an example, we use UR5 robot to verify the effectiveness and feasibility of this design. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： End effectors

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