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MRCap: Multi-modal and Multi-level Relationship-based Dense Video Captioning

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MRCap: Multi-modal and Multi-level Relationship-based Dense ...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Wei Chen Jianwei Niu Xuefeng Liu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Dense video captioning, with the objective of describing a sequence of events in a video, has received much attention recently. As events in a video are highly correlated, leveraging relationships among events helps generate coherent captions. To utilize relationships among events, existing methods mainly enrich event representations with their context, either in the form of vision (i.e., video segments) or combining vision and language (i.e., captions). However, these methods do not explicitly exploit the correspondence between these two modalities. Moreover, the video-level context spanning multiple events is not fully exploited. In this paper, we propose MRCap, a novel relationship-based model for dense video captioning. The key of MRCap is a multi-modal and multi-level event relationship module (MMERM). MMERM exploits the correspondence between vision and language at both the event level and the video level via contrastive learning. Experiments on ActivityNet Captions and YouCook2 datasets demonstrate that MRCap achieves state-of-the-art performance.

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Understanding Wireless Charger Networks: Concepts, Current Research, and Future Directions

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IEEE Communications Surveys and Tutorials 2024年

作者： Ren, Meixuan Dai, Haipeng Liu, Tang Deng, Xianjun Dou, Wanchun Yang, Yuanyuan Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Stony Brook University Department of Electrical and Computer Engineering NY11794 United States

Wireless Charger Network (WCN) emerges as a promising networking paradigm, employing wireless chargers with Wireless Power Transfer (WPT) technology to provide long-term and sustainable energy supply for future networks. Although extensive research has been conducted in this area over the last decade, there is currently no comprehensive survey to compile the latest literature and provide insights into future research directions. To fill this gap, our survey explores the recent developments in the active research area of WCNs. This paper starts by providing a framework of WCNs in detail, covering aspects of network architecture, various charging models, network design issues, and typical applications of WCNs. Then, we give an overview of charger deployment schemes, focusing on omnidirectional, directional, non-radiative, and heterogeneous charger deployments. We also provide an overview of charging scheduling schemes, encompassing power control, time allocation, energy beamforming, and multi-resource scheduling. Moreover, we explore communication optimization schemes, including Medium Access Control (MAC) protocols, routing protocols, broadcast transmission, and data collection. Finally, we highlight some future research directions and present corresponding open issues to advance the research on WCNs. © 1998-2012 IEEE.

关键词： Inductive power transmission

An Effective Two-stage Training Paradigm Detector for Small Dataset

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arXiv 2023年

作者： Wang, Zheng Xie, Dong Wang, Hanzhi Tian, Jiang AI Lab Lenovo Research Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science Yale University New HavenCT United States

Learning from the limited amount of labeled data to the pre-train model has always been viewed as a challenging task. In this report, an effective and robust solution, the two-stage training paradigm YOLOv8 detector (TP-YOLOv8), is designed for the object detection track in VIPriors Challenge 2023. First, the backbone of YOLOv8 is pre-trained as the encoder using the masked image modeling technique. Then the detector is fine-tuned with elaborate augmentations. During the test stage, test-time augmentation (TTA) is used to enhance each model, and weighted box fusion (WBF) is implemented to further boost the performance. With the well-designed structure, our approach has achieved 30.4% average precision from 0.50 to 0.95 on the DelftBikes test set, ranking 4th on the leaderboard. © 2023, CC BY.

关键词： Object detection

Frequency-Guided Network for Low-contrast Staining-free Dental Plaque Segmentation

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Frequency-Guided Network for Low-contrast Staining-free Dent...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yiming Jiang Wenfeng Song Shuai Li Yuming Yang Bin Xia Aimin Hao Hong Qin The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China The School of Computer Science Beijing Information Science and Technology University Beijing China The Department of Pediatric Dentistry Peking University School and Hospital of Stomatology Beijing China The Department of Computer Science Stony Brook University New York USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Traditional dental plaque detection relies on medical staining reagents and professional intervention. Deep learning-based automatic staining-free dental plaque segmentation provides an alternative for patients to perform plaque detection at home without staining reagents. However, existing methods still struggle with low-contrast visual features between unstained plaque and healthy teeth. To address this, we propose a Frequency-Guided Network (FGN) for low-contrast staining-free dental plaque segmentation. We observe that dental plaque tends to concentrate specifically near the junction between the teeth and the gingiva. This junction demonstrates abrupt changes in pixel values, indicating high-frequency regions in the image. In other words, dental plaque tends to appear near the high-frequency regions of oral endoscope images. Exploiting this characteristic, we employ a frequency-guided decoupling module to separate the image into high-frequency and low-frequency regions automatically and expand the high-frequency region to encompass nearby potential dental plaque. Then we supervise two regions individually to specifically focus on the expended high-frequency region for localizing nearby dental plaque. Additionally, we propose a high-to-low frequency multiple tasks framework. In the first phase, the network segments the teeth region, and then we input the teeth mask into the second phase. In the second stage, the teeth mask allows us to have a higher frequency at the junction between the teeth and gums, thereby enhancing the effectiveness of frequency-guided decoupling. Furthermore, FGN integrates a frequency-driven refinement module to enhance the guidance quality of the teeth mask for the second phase. Extensive evaluations of the oral endoscope dataset demonstrate that our method outperforms existing high-performance segmentation methods. User studies also confirm that our approach achieves superior results to experienced dentists. https://frequency-guided-netw

关键词： Image segmentation Visualization Endoscopes Image edge detection Neural networks Teeth Dentistry Junctions Faces Biomedical imaging

Few-Shot Open-Set Object Detection via Conditional Evidence Decoupling

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arXiv 2024年

作者： Wu, Zhaowei Su, Binyi Zhang, Hua Zhou, Zhong The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Zhongguancun Laboratory Beijing100190 China

Few-shot Open-set Object Detection (FOOD) poses a significant challenge in real-world scenarios. It aims to train an open-set detector under the condition of scarce training samples, which can detect known objects while rejecting unknowns. Under this challenging scenario, the decision boundaries of unknowns are difficult to learn and often ambiguous. To mitigate this issue, we develop a two-stage open-set object detection framework with prompt learning, which delves into conditional evidence decoupling for the unknown rejection. Specifically, we propose an Attribution-Gradient-based Pseudo-unknown Mining (AGPM) method to select region proposals with high uncertainty, which leverages the discrepancy in attribution gradients between known and unknown classes, alleviating the inadequate unknown distribution coverage of training data. Subsequently, we decouple known and unknown properties in pseudo-unknown samples to learn distinct knowledge with proposed Conditional Evidence Decoupling (CED), which enhances separability between knowns and unknowns. Additionally, we adjust the output probability distribution through Abnormal Distribution Calibration (ADC), which serves as a regularization term to establish robust decision boundaries for the unknown rejection. Our method has achieved superior performance over previous state-of-the-art approaches, improving the mean recall of unknown class by 7.24% across all shots in VOC10-5-5 dataset settings and 1.38% in VOC-COCO dataset settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

Affine particle-in-cell method for two-phase liquid simulation

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virtual reality & Intelligent Hardware 2021年第2期3卷 105-117页

作者： Luan LYU Wei CAO Enhua WU Zhixin YANG Faculty of Science and Technology University of MacaoMacao 999078China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences(CAS)&University of CASBeijing 100190China Guangzhou Greater Bay Area Virtual Reality Research Institute Guangzhou 510700China

Background The interaction of gas and liquid can produce many interesting phenomena,such as bubbles rising from the bottom of the *** simulation of two-phase fluids is a challenging topic in computer *** animate the interaction of a gas and liquid,MultiFLIP samples the two types of particles,and a Euler grid is used to track the interface of the liquid and ***,MultiFLIP uses the fluid implicit particle(FLIP)method to interpolate the velocities of particles into the Euler grid,which suffer from additional noise and *** To solve the problem caused by fluid implicit particles(FLIP),we present a novel velocity transport technique for two individual particles based on the affine particle-in-cell(APIC)***,we design a weighed coupling method for interpolating the velocities of liquid and gas particles to the Euler grid such that we can apply the APIC method to the simulation of a two-phase ***,we introduce a narrowband method to our system because MultiFLIP is a time-consuming approach owing to the large number of *** Experiments show that our method is well integrated with the APIC method and provides a visually credible two-phase fluid *** The proposed method can successfully handle the simulation of a two phase fluid.

关键词： Fluid simulation Two-Phase flow Affine particle-in-cell method

Link-identified Routing Architecture in Space

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arXiv 2024年

作者： Zhang, Hefan Wang, Zhiyuan Zhang, Shan Meng, Qingkai Luo, Hongbin The School of Computer Science and Engineering Beihang University Beijing100191 China The State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China The State Key Laboratory of Software Development Environment Beijing100191 China The Institute of Artificial Intelligence Beihang University Beijing100191 China The School of Cyber Science and Technology Beihang University Beijing100191 China

Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient packet delivery. In this paper, we propose a Link-identified Routing (LiR) architecture for LEO satellite networks. The LiR architecture leverages the deterministic neighbor relation of LEO constellations, and identifies each inter-satellite link (ISL). Moreover, LiR architecture adopts source-route-style forwarding based on in-packet bloom filter (BF). Each satellite could efficiently encode multiple ISL identifiers via an in-packet BF to specify the end-to-end path for the packets. Due to false positives caused by BF, the more ISLs are encoded at a time, the more redundant forwarding cases emerge. Based on the topology characteristics, we derive the expected forwarding overhead in a closed-form and propose the optimal encoding policy. To accommodate link-state changes in LEO satellite networks, we propose the on-demand rerouting scheme and the on-demand detouring scheme to address the intermittent ISLs. We also elaborate how to take advantage of LiR architecture to achieve seamless handover for ground-satellite links (GSLs). Finally, we conduct extensive numerical experiments and packet-level simulations to verify our analytical results and evaluate the performance of the LiR architecture. © 2024, CC BY.

关键词： Satellite links

Gaze-Vergence-Controlled See-Through vision in Augmented reality

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arXiv 2022年

作者： Wang, Zhimin Zhao, Yuxin Lu, Feng The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Peng Cheng Laboratory Shenzhen518055 China

— Augmented reality (AR) see-through vision is an interesting research topic since it enables users to see through a wall and see the occluded objects. Most existing research focuses on the visual effects of see-through vision, while the interaction method is less studied. However, we argue that using common interaction modalities, e.g., midair click and speech, may not be the optimal way to control see-through vision. This is because when we want to see through something, it is physically related to our gaze depth/vergence and thus should be naturally controlled by the eyes. Following this idea, this paper proposes a novel gaze-vergence-controlled (GVC) see-through vision technique in AR. Since gaze depth is needed, we build a gaze tracking module with two infrared cameras and the corresponding algorithm and assemble it into the Microsoft HoloLens 2 to achieve gaze depth estimation. We then propose two different GVC modes for see-through vision to fit different scenarios. Extensive experimental results demonstrate that our gaze depth estimation is efficient and accurate. By comparing with conventional interaction modalities, our GVC techniques are also shown to be superior in terms of efficiency and more preferred by users. Finally, we present four example applications of gaze-vergence-controlled see-through vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Augmented reality

GraphFed: A Personalized Subgraph Federated Learning Framework for Non-IID Graphs

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GraphFed: A Personalized Subgraph Federated Learning Framewo...

IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Pan Deng Xuefeng Liu Jianwei Niu Chunming Hu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Information Engineering Zhengzhou University Research Institute of Industrial Technology Zhengzhou China Zhengzhou University Zhengzhou China

Recently, federated graph learning has attracted significant attention, as subgraphs of a global graph may often distribute across different institutions and are subject to privacy restrictions. However, inevitable data heterogeneity among sub-graphs can lead to poor performance of Graph Neural Networks (GNNs) trained by vanilla federated learning algorithm, which has been neglected before. To address this problem we look at subgraphs and find that there are usually overlapping nodes among them. Inspired by the common overlapping nodes among subgraphs, we propose GraphFed, a subgraph federated learning framework which solve the challenge in terms of both data augmentation and personalized federated learning. GraphFed uses common overlapping nodes among subgraphs to locate lost node information and applies data augmentation by retrieving lost nodes and forming common node sets that mitigates data distribution difference. Furthermore, GraphFed treats the overlapping nodes as bridges to calculate the distance among different subgraphs, which implies the similarity of the graph data distribution. Then, we use the distance to perform personalized federated learning. Empirical results and analysis on three real-world graph datasets with graph federated learning settings demonstrate the effectiveness of our proposed framework.

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