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SSRN 2024年

作者： Shi, Weimin Xiong, Yuan Wang, Qianwen Jiang, Han Zhou, Zhong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Zhongguancun Laboratory Beijing China

3D shape representation using mesh data is essential in various applications, such as virtual reality and simulation technologies. Current methods extracting features from mesh edges or faces struggle with complex 3D models due to edge-based approaches missing global context and face-based methods overlooking variations in adjacent areas, which affects overall precision. To address these issues, we propose the Feature Discrimination and Context Propagation Network (FDCPNet), a novel approach that synergistically integrates local and global features in mesh datasets. FDCPNet is composed of two modules: 1) Feature Discrimination Module (FDM), which employs an attention mechanism to enhance the identification of key local features, 2) Context Propagation Module (CPM) enriches key local features by integrating global contextual information, facilitating a more detailed and comprehensive representation of crucial areas within the mesh model. Experiments on the Manifold40 dataset validate FDCPNet's effectiveness, showing a 1.9% improvement in classification accuracy over the baseline MeshNet, achieving 90.3%. Furthermore, even with reduced mesh face numbers and limited training data, FDCPNet achieves promising results, showing its robustness in variable complexity scenarios. © 2024, The Authors. All rights reserved.

关键词： Complex networks

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Semantic part segmentation of single-view point cloud

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Science China(Information Sciences) 2020年第12期63卷 251-253页

作者： Haotian PENG Bin ZHOU Liyuan YIN Kan GUO Qinping ZHAO State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang University

As a classic topic in computer graphics, the semantic part segmentation of 3D data is helpful for3D part-level editing and modeling. Single-views point cloud is the raw format of 3D data. Giving each point a semantic annotation in single-view point cloud, i.e., single-view point cloud semantic part segmentation, is meaningful and challenging.

关键词： computers and graphics single-view point cloud annotation transfer semantic part segmentation category independent matching point clouds registration

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G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

G-MAP: General Memory-Augmented Pre-trained Language Model f...

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

作者： Wan, Zhongwei Yin, Yichun Zhang, Wei Shi, Jiaxin Shang, Lifeng Chen, Guangyong Jiang, Xin Liu, Qun Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Science China University of Chinese Academy of Sciences China Huawei Noah's Ark Lab Huawei Cloud Computing Zhejiang Lab China

Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this Domain-Adaptive Pre-Training (DAPT;Gururangan et al. (2020)) tends to forget the previous general knowledge acquired by general PLMs, which leads to a catastrophic forgetting phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of General Memory-Augmented Pre-trained Language Model (G-MAP), which augments the domain-specific PLM by a memory representation built from the frozen general PLM without losing any general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmented strategies are explored to build the memory representation and then adaptively fuse it into the domain-specific PLM. We demonstrate the effectiveness of G-MAP on various domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed G-MAP can achieve SOTA results on all tasks. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment

arXiv

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arXiv 2024年

作者： Qi, Yunshan Zhu, Lin Zhao, Yifan Bao, Nan Li, Jia State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

Neural Radiance Fields (NeRF) achieves impressive 3D representation learning and novel view synthesis results with high-quality multi-view images as input. However, motion blur in images often occurs in low-light and high-speed motion scenes, which significantly degrades the reconstruction quality of NeRF. Previous deblurring NeRF methods struggle to estimate pose and lighting changes during the exposure time, making them unable to accurately model the motion blur. The bio-inspired event camera measuring intensity changes with high temporal resolution makes up this information deficiency. In this paper, we propose Event-driven Bundle Adjustment for Deblurring Neural Radiance Fields (EBAD-NeRF) to jointly optimize the learnable poses and NeRF parameters by leveraging the hybrid event-RGB data. An intensity-change-metric event loss and a photo-metric blur loss are introduced to strengthen the explicit modeling of camera motion blur. Experiments on both synthetic and real-captured data demonstrate that EBAD-NeRF can obtain accurate camera trajectory during the exposure time and learn a sharper 3D representations compared to prior works. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Gaze Pattern Genius: Gaze-Driven VR Interaction Using Unsupervised Domain Adaption

Gaze Pattern Genius: Gaze-Driven VR Interaction Using Unsupe...

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virtual reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Kexin Wang Yang Gao Wenfeng Song Yuecheng Li Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Computer School Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

This research advocates shifting VR interaction to gaze-driven interaction, a more intuitive alternative to traditional controls like VR controllers or gestures. Our focus is on enhancing neural network recognition accuracy, especially with limited user-specific gaze data. We introduce a novel framework for capturing gaze gesture patterns and propose a template dataset concept to boost neural training. Our unsupervised domain adaptation model, blending template depth and sparse user data authenticity, consistently excels in recognizing gaze patterns across diverse users. Rigorous benchmarking against leading architectures consistently shows our method outperforming. Empirical user studies confirm: gaze-driven interactions not only elevate VR experiences but also redefine immersive VR control dynamics.

关键词： Training Adaptation models Three-dimensional displays Conferences Neural networks virtual reality User interfaces

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Distributional Estimation of Data Uncertainty for Surveillance Face Anti-spoofing

arXiv

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arXiv 2023年

作者： Huang, Mouxiao The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Shenzhen China

Face recognition systems have become increasingly vulnerable to security threats in recent years, prompting the use of Face Anti-spoofing (FAS) to protect against various types of attacks, such as phone unlocking, face payment, and self-service security inspection. While FAS has demonstrated its effectiveness in traditional settings, securing it in long-distance surveillance scenarios presents a significant challenge. These scenarios often feature low-quality face images, necessitating the modeling of data uncertainty to improve stability under extreme conditions. To address this issue, this work proposes Distributional Estimation (DisE), a method that converts traditional FAS point estimation to distributional estimation by modeling data uncertainty during training, including feature (mean) and uncertainty (variance). By adjusting the learning strength of clean and noisy samples for stability and accuracy, the learned uncertainty enhances DisE's performance. The method is evaluated on SuHiFiMask [1], a large-scale and challenging FAS dataset in surveillance scenarios. Results demonstrate that DisE achieves comparable performance on both ACER and AUC metrics. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Why go full? elevating federated learning through partial network updates 24

Why go full? elevating federated learning through partial ne...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haolin Wang Xuefeng Liu Jianwei Niu Wenkai Guo Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China and Zhongguancun Laboratory Beijing China Center for AI Business Innovation Department of Management Science and Systems University at Buffalo Buffalo New York

ISBN: (纸本)9798331314385

Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditional federated learning, the entire parameter set of local models is updated and averaged in each training round. Although this full network update method maximizes knowledge acquisition and sharing for each model layer, it prevents the layers of the global model from cooperating effectively to complete the tasks of each client, a challenge we refer to as layer mismatch. This mismatch problem recurs after every parameter averaging, consequently slowing down model convergence and degrading overall performance. To address the layer mismatch issue, we introduce the FedPart method, which restricts model updates to either a single layer or a few layers during each communication round. Furthermore, to maintain the efficiency of knowledge acquisition and sharing, we develop several strategies to select trainable layers in each round, including sequential updating and multi-round cycle training. Our theoretical analysis and experimental results show that the FedPart method significantly outperforms traditional full-network update strategies, achieving faster convergence, greater accuracy, and reduced communication and computational overhead.

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Pedestrian attribute recognition as label-balanced multi-label learning 24

Pedestrian attribute recognition as label-balanced multi-lab...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yibo Zhou Hai-Miao Hu Yirong Xiang Xiaokang Zhang Haotian Wu Beijing Key Laboratory of Digital Media School of Computer Science and Engineering and State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Key Laboratory of Digital Media School of Computer Science and Engineering and State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China and Hangzhou Innovation Institute of Beihang University Hangzhou China The University of Manchester UK

Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions lean greatly towards the side of majority labels; (2) semantics imbalance: model is easily overfitted on the under-represented attributes due to their insufficient semantic diversity. To render perfect label balancing, we propose a novel framework that successfully decouples label-balanced data re-sampling from the curse of attributes co-occurrence, i.e., we equalize the sampling prior of an attribute while not biasing that of the co-occurred others. To diversify the attributes semantics and mitigate the feature noise, we propose a Bayesian feature augmentation method to introduce true in-distribution novelty. Handling both imbalances jointly, our work achieves best accuracy on various popular benchmarks, and importantly, with minimal computational budget.

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CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction 29

CETA: A Consensus Enhanced Training Approach for Denoising i...

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29th International Conference on Computational Linguistics, COLING 2022

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

Distantly supervised relation extraction aims to extract relational facts from texts but suffers from noisy instances. Existing methods usually select reliable sentences that rely on potential noisy labels, resulting in wrongly selecting many noisy training instances or underutilizing a large amount of valuable training data. This paper proposes a sentence-level DSRE method beyond typical instance selection approaches by preventing samples from falling into the wrong classification space on the feature space. Specifically, a theorem for denoising and the corresponding implementation, named Consensus Enhanced Training Approach (CETA), are proposed in this paper. By training the model with CETA, samples of different classes are separated, and samples of the same class are closely clustered in the feature space. Thus the model can easily establish the robust classification boundary to prevent noisy labels from biasing wrongly labeled samples into the wrong classification space. This process is achieved by enhancing the classification consensus between two discrepant classifiers and does not depend on any potential noisy labels, thus avoiding the above two limitations. Extensive experiments on widely-used benchmarks have demonstrated that CETA significantly outperforms the previous methods and achieves new state-of-the-art results. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Classification (of information)

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