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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Fan, Shengda Mo, Shasha Niu, Jianwei School of Cyber Science and Technology Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

Document-level relation extraction (DocRE) aims at extracting relations of all entity pairs in a document. A key challenge to DocRE lies in the complex interdependency between the relations of entity pairs. Unlike most prior efforts focusing on implicitly powerful representations, the recently proposed LogiRE (Ru et al., 2021) explicitly captures the interdependency by learning logical rules. However, LogiRE requires extra parameterized modules to reason merely after training backbones, and this disjointed optimization of backbones and extra modules may lead to sub-optimal results. In this paper, we propose MILR, a logic enhanced framework that boosts DocRE by Mining and Injecting Logical Rules. MILR first mines logical rules from annotations based on frequencies. Then in training, consistency regularization is leveraged as an auxiliary loss to penalize instances that violate mined rules. Finally, MILR infers from a global perspective based on integer programming. Compared with LogiRE, MILR does not introduce extra parameters and injects logical rules during both training and inference. Extensive experiments on two benchmarks demonstrate that MILR not only improves the relation extraction performance (1.1%-3.8% F1) but also makes predictions more logically consistent (over 4.5% Logic). More importantly, MILR also consistently outperforms LogiRE on both counts. Code is available at https://***/XingYing-stack/MILR. © 2022 Association for Computational Linguistics.

关键词： Integer programming

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Research on Crowdsourcing Price Game Model in Crowd Sensing

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computers, Materials & Continua 2021年第8期68卷 1769-1784页

作者： Weijin Jiang Xiaoliang Liu Dejia Shi Junpeng Chen Yongxia Sun Liang Guo School of Computer Science and Technology Hunan University of Technology and BusinessChangsha410205China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Changsha410205China School of Bioinformatics University of MinnesotaTwin Cities15213USA

Crowd-Sensing is an innovative data acquisition method that combines the perception of mobile devices with the idea of *** is a new application mode under the development of the Internet of *** perceptual data that mobile users can provide is *** crowdsourcing parties will share this limited data,but the cost that the crowdsourcing party can pay is limited,and enough mobile users are needed to complete the perceptual task,making the group wisdom is really *** this process,there is bound to be a game between the crowds and the mobile *** of the existing researches consider a group-aware system.A group of mobile users will directly share or compete for the opportunity of the crowd-holders to do tasks and get paid,the behavior of multiple crowd-source parties,and their bilateral interaction with mobile *** research is not clear enough and there is no targeted *** paper will model and analyze the dynamic evolution process of crowd sensing *** on the unique characteristics of crowd-source non-cooperative game and crowd-sourced Nash equilibrium,we will develop a perceptual plan for mobile users and use the stability analysis of iterative algorithms to explore a way to better match the capabilities of mobile users and the needs of crowdsourced *** theoretical analysis and simulation results verify the dynamic evolution model of crowdsourcing in group perception and propose a method to improve the efficiency of crowdsourcing.

关键词： Crowd-sensing crowdsourcing incentives sensor networks

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MvEchoSeg: One-shot In-context Learning for Multi-view Echocardiography Segmentation

MvEchoSeg: One-shot In-context Learning for Multi-view Echoc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Guo, Yuting Duan, Ya Song, Wenfeng Li, Nannan Li, Aili Li, Shuai Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China University of Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Beijing Information Science and Technology University School of Computer Science Beijing100192 China Dalian Maritime University School of Information Science and Technology Dalian116000 China China-Japan Friendship Hospital Beijing100029 China

ISBN: (纸本)9798350386226

Echocardiography is the clinical standard for evaluation of cardiac morphology, and function, and providing hemodynamic parameters in patients with known or suspected heart disease. Due to the diversity and complexity of diagnostic tasks, comprehensive interpretation of echocardiography often requires multi-view imaging for integrated metric analysis. Deep learning methods have become the mainstream approach for echocardiography segmentation. However, achieving segmentation of multi-view echocardiography still requires a substantial amount of annotation. To remedy this, we propose a one-shot in-context learning network mvEchoSeg for multi-view echocardiography segmentation. This network requires only one annotated image for each view. Specifically, we propose a Task Prompt Identifier (TPI) module to identify the task type of the image and allocate the most precise task prompt for it, with minimal adaptation of CLIP and few-shot strategies. Additionally, we leverage a unified In-Context Model (ICM) capable of performing a diverse set of echocardiography segmentation tasks automatically. Using fine-tuning and low-rank adapters improved the performance of the pre-trained model, achieving significant results with minimal training cost. Furthermore, we collect a multi-view echocardiography dataset (MVECD) with 8 views to evaluate our method. The results show an improvement of more than 10% in the DICE score compared to the SOTA foundational medical image segmentation models. To our knowledge, this is the first exploration of a one-shot model for multi-view echocardiography segmentation. Our codes and models are available at https://***/stellating/mvEchoSeg. © 2024 IEEE.

关键词： Image segmentation

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Relational Clustering-Based Parallel Spaces Construction and Embedding for Dynamic Knowledge Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Yao Zhang, Yongfei Beihang University Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beijing100191 China Beihang University School of Computer Science and Engineering China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Pengcheng Laboratory Shenzhen518055 China

With the increasing amount of data in various domains, knowledge graphs (KGs) have become powerful tools for representing complex and heterogeneous information in a structured way, and for extracting valuable information from knowledge graphs through embedding techniques to support downstream tasks such as recommendation and Q&A systems. Knowledge graphs consist of triples that are continuously added as knowledge is updated. However, most existing embedding models are designed for static graphs, requiring the entire model to be retrained for each update, which is time-consuming. Existing global dynamic embedding models focus on exploiting the structural and relational information of the whole graph to achieve embedding quality, resulting in reduced dynamic efficiency. To address this problem, we propose a relational clustering-based parallel space model in which knowledge from different domains is embedded in different subspaces, allowing each subspace to focus on the data characteristics of a specific domain, thereby improving the quality of knowledge. Second, the new data only affects some subspaces but not the performance of other spaces, improving the model's adaptability to dynamics. Furthermore, we employ two incremental approaches based on the type of added data to improve the efficiency of dynamic embedding while ensuring that the added data preserves the characteristics of the parallel space. The experimental results show that the dynamic embedding efficiency of our model is improved by an average of 50.3% compared to the SOTA dynamic embedding model for the link prediction task. Particularly on FB15K, our model not only improves the efficiency by 41% but also increases the accuracy by 7.5%, demonstrating the accuracy and efficiency of our model. © 2015 IEEE.

关键词： Knowledge graph

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Locomotion perception and redirection

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virtual reality & Intelligent Hardware 2021年第6期3卷 I0003-I0004页

作者： Miao WANG Songhai ZHANG Shimin HU State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Locomotion is a fundamental interaction technique that allows free navigation in virtual scenes.A large body of literature has demonstrated that natural locomotion experience can significantly improve the sense of presence and help users achieve better performance in tasks such as searching and *** the upcoming metaverse era,locomotion will play a critical role in the success of large-scale interactive *** and redirection are two tightly connected problems in virtual *** a limited physical condition and a target virtual scene,how to make the perceived locomotion as natural as possible,and further unknowingly redirect the user in the physical world is an open *** this special issue,we have selected five papers that provide their latest updates for research problems of view control,virtual jumping,climbing,and searching.

关键词： redirect interactive motion

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Local Context and Dimensional Relation Aware Transformer Network for Continuous Affect Estimation

Local Context and Dimensional Relation Aware Transformer Net...

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IEEE International Conference on Image Processing

作者： Shuo Yang Yongtang Bao Yue Qi State Key Laboratory of Virtual Reality Technology and Systems Beihang University China College of Computer Science and Engineering Shandong University of Science and Technology China Virtual Reality Research Institute Beihang University of Qingdao Research Institute China

In recent years, video-based continuous affect estimation has received more attention in computer vision. Therefore, how to robustly and accurately model the temporal information during facial expression change is crucial. Hence, we propose a transformer network that incorporates both local context and dimensional correlation to model visual information in an efficient manner. Specifically, noise, such as instantaneous head poses and lighting changes, may affect the model’s performance due to the local context insensitivity of the transformer’s self-attention layer. Therefore, a local-wise transformer encoder is adopted to enhance the transformer’s ability to capture local contextual information. In addition, considering the prior knowledge of the correlation between valence and arousal,we design the va-relevance bootstrap module and the corresponding valence-arousal relevance loss (va loss). Experiments on Aff-Wild2 and AFEW-VA datasets show the superior performance of our method for continuous affect estimation.

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RGB-D Salient Object Detection with Ubiquitous Target Awareness

RGB-D Salient Object Detection with Ubiquitous Target Awaren...

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作者： Zhao, Yifan Zhao, Jiawei Li, Jia Chen, Xiaowu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt to solve the RGB-D salient object detection problem with a novel depth-awareness framework. This framework only relies on RGB data in the testing phase, utilizing captured depth data as supervision for representation learning. To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales. Besides its high performance, our proposed UTA network is depth-free for inference and runs in real-time with 43 FPS. Experimental evidence demonstrates that our proposed network not only surpasses the state-of-the-art methods on five public RGB-D SOD benchmarks by a large margin, but also verifies its extensibility on five public RGB SOD benchmarks. © 1992-2012 IEEE.

关键词： Object detection

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RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction

RepMode: Learning to Re-Parameterize Diverse Experts for Sub...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Donghao Zhou Chunbin Gu Junde Xu Furui Liu Qiong Wang Guangyong Chen Pheng-Ann Heng Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences The Chinese University of Hong Kong Zhejiang Lab

In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, subcellular structures vary considerably in size, which causes the multi-scale issue of SSP. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode can achieve state-of-the-art overall performance in SSP.

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MLLMReID: Multimodal Large Language Model-based Person Re-identification

arXiv

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arXiv 2024年

作者： Yang, Shan Zhang, Yongfei Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

Multimodal large language models (MLLM) have achieved satisfactory results in many tasks. However, their performance in the task of ReID (ReID) has not been explored to date. This paper will investigate how to adapt them for the task of ReID. An intuitive idea is to fine-tune MLLM with ReID image-text datasets, and then use their visual encoder as a backbone for ReID. However, there still exist two apparent issues: (1) Designing instructions for ReID, MLLMs may overfit specific instructions, and designing a variety of instructions will lead to higher costs. (2) When fine-tuning the visual encoder of a MLLM, it is not trained synchronously with the ReID task. As a result, the effectiveness of the visual encoder fine-tuning cannot be directly reflected in the performance of the ReID task. To address these problems, this paper proposes MLLMReID: Multimodal Large Language Model-based ReID. Firstly, we proposed Common Instruction, a simple approach that leverages the essence ability of LLMs to continue writing, avoiding complex and diverse instruction design. Secondly, we propose a multi-task learning-based synchronization module to ensure that the visual encoder of the MLLM is trained synchronously with the ReID task. The experimental results demonstrate the superiority of our method. © 2024, CC BY-NC-ND.

关键词： Learning systems

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SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition 42

SVDFed: Enabling Communication-Efficient Federated Learning ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Haolin Liu, Xuefeng Niu, Jianwei Tang, Shaojie Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou China Naveen Jindal School Management University of Texas at Dallas Richardson United States

ISBN: (纸本)9798350334142

Federated learning (FL) is an emerging paradigm of distributed machine learning. However, when applied to wireless network scenarios, FL usually suffers from high communication cost because clients need to transmit their updated gradients to a server in every training round. Although many gradient compression techniques like sparsification and quantization are proposed, they compress clients' gradients independently, without considering the correlations among gradients. In this paper, we propose SVDFed, a collaborative gradient compression framework for FL. SVDFed utilizes Singular Value Decomposition (SVD) to find a few basis vectors, whose linear combination can well represent clients' gradients at a certain round. Due to the correlations among gradients, these basis vectors can still well approximate new gradients in many subsequent rounds. With the help of basis vectors, clients only need to upload the coefficients of the linear combination to the server, which greatly reduces communication cost. In addition, SVDFed leverages the classical PID (Proportional, Integral, Derivative) control to determine the proper time to update basis vectors to maintain their representation ability. Through experiments, we demonstrate that SVDFed outperforms existing gradient compression methods in FL. For example, compared to a popular gradient quantization method QSGD, SVDFed can reduce the communication overhead by 66 % and pending time by 99 %. © 2023 IEEE.

关键词： Singular value decomposition

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