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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Gao, Ruicheng Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Jingdezhen Research Institute Beihang University Jingdezhen333000 China Qingdao Research Institute Beihang University Qingdao266104 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The objective of physics-based differentiable rendering (PBDR) is to propagate gradients between scene parameters and the intensities of image pixels in a manner that is physically correct. The gradients obtained can be applied in optimization algorithms for the reconstruction of 3D geometry or materials, or they can be further propagated into neural network to learn neural representations of the scene. However, applying automatic differentiation techniques directly to the primary rendering process will result in biased gradients, as the rendering integral contains moving high-dimensional discontinuities. Based on how these discontinuities are managed-either implicitly or explicitly-existing PBDR methods can be categorized into two groups: reparameterization methods and boundary sampling methods. Boundary sampling methods need to construct paths that have one segment tangent to the geometry being differentiated in order to estimate a boundary integral to address the discontinuities explicitly. Such paths are usually constructed by sampling the tangent segment first and then extending it to complete the paths for subsequent processing. Fortunately, the number of dimensions in the space composed of such tangent segments is only three. In scenes comprised solely of triangle meshes, the first dimension is used to parameterize all the edges on the mesh, which determines a point on the tangent segment. The remaining two dimensions are used to parameterize the direction of the tangent segment. However, state-of-the-art boundary sampling methods parameterize the first dimension uniformly, which is inefficient because only a small portion of the edges contributes to the boundary integral, resulting in wasted parameter space. In this paper, we parameterize the first dimension by considering both edge length and contributions, thereby allocating more parameter space to important edges. Experiments demonstrate that our methods achieve lower variance gradients in the forward dif

关键词： Mesh generation

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Super-resolution of Solar Magnetograms Using Deep Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 218-229页

作者： Fengping Dou Long Xu Zhixiang Ren Dong Zhao Xinze Zhang State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng National Laboratory Shenzhen 518000China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Currently,data-driven models of solar activity forecast are investigated extensively by using machine *** model training,it is highly demanded to establish a large database which may contain observations coming from different instruments with different spatio-temporal *** this paper,we employ deep learning models for super-resolution(SR)of magnetogram of Michelson Doppler Imager(MDI)in order to achieve the same spatial resolution of Helioseismic and Magnetic Imager(HMI).First,a generative adversarial network(GAN)is designed to transfer characteristics of MDI onto downscaled HMI,getting low-resolution HMI magnetogram in the same domain as ***,with the paired low-resolution and high-resolution HMI magnetograms,another GAN is trained in a supervised learning way,which consists of two streams,one is for generating high-fidelity image content,the other is explicitly optimized for generating elaborate image ***,these two streams work together to guarantee both high-fidelity and photorealistic super-resolved *** results demonstrate that the proposed method can generate super-resolved magnetograms with perceptual-pleasant visual ***,the best PSNR,LPIPS,RMSE,comparable SSIM and CC are obtained by the proposed *** source code and data set can be accessed via https://***/filterbank/SPSR.

关键词： techniques:image processing Sun:magnetic fields Sun:atmosphere

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

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

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

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

ISBN: (纸本)9798350386226

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

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Dual Consistency Learning for Semi-Supervised Airway Segmentation 2

Dual Consistency Learning for Semi-Supervised Airway Segment...

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2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Li, Mingshuang Yuan, Yunyi Wang, Qiong Qian, Yinling Zhu, Lei Southern University of Science and Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen China Guangzhou China

ISBN: (纸本)9798350386639

Airway segmentation serves as an essential foundational process for both the diagnosis of lung conditions and the navigation of surgical interventions. Although numerous attempts have been proposed to address airway segmentation, the application of semi-supervised learning in this task has not yet been extensively investigated. In this study, we introduce an innovative semi-supervised learning framework that incorporates dual consistency learning during the re-training. Dual consistency learning exploits image-level and feature-level perturbations simultaneously to fully and efficiently extract additional information from unlabeled data. Specifically, image-level consistency learning employs a novel frequency domain data augmentation scheme to enforce topological feature capturing, and feature-level consistency learning could explore a broader perturbation space, thus realizing discriminative representation extraction from feature space. We carry out comprehensive experiments across various public datasets to substantiate the efficacy of our proposed methodology. The results illustrate that our framework attains remarkable performance levels, surpassing the current state-of-the-art approaches. © 2024 IEEE.

关键词： Self-supervised learning

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A Doctors Behavior Aware and Domain Knowledge Driven Model for Medical Report Generation

A Doctors Behavior Aware and Domain Knowledge Driven Model f...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Han Niu, Jianwei Liu, Xuefeng Wang, Yong Beihang University School of Computer Science Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beijing China Zhongguancun Laboratory Beijing China Peking Union Medical College Chinese Academy of Medical Sciences Beijing China

ISBN: (纸本)9798350337488

When doctors write a medical report, they first focus on key regions in the image, which usually contain abnormal information. Then, they write the report based on their experience and professional knowledge. Some existing medical report generation methods design corresponding schemes to model this process, but they still have some drawbacks. Some of them adopt the attention map to model the doctors' image-reading behavior, which requires additional cropping and feature extraction operations, bringing many extra computations. Other methods manually construct knowledge graphs to model the doctors' domain knowledge, which requires manual construction for different disease domains and has less generalization. Therefore, We propose DEKG, a doctors behavior aware and domain knowledge driven model, to improve the quality of generated reports by modeling the fixed pattern of doctors when writing reports. Specifically, feature clustering is utilized to model the doctors' image-reading behavior that only brings fewer computations, and then we capture key regions with possible abnormalities. We also design a method to automatically construct domain knowledge graphs, which can quickly accomplish the construction process for different disease domains without human intervention. Extensive experiments on datasets from different disease domains demonstrate that DEKG achieves competitive results with state-of-the-art methods and can generate high-quality reports. © 2023 IEEE.

关键词： Feature Clustering Knowledge Graph Medical Report Generation Transformer

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Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes

Neural 3D Strokes: Creating Stylized 3D Scenes with Vectoriz...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao-Bin Duan Miao Wang Yan-Xun Li Yong-Liang Yang State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Zhongguanchun Laboratory Department of Computer Science University of Bath

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present Neural 3D Strokes, a novel technique to gen-erate stylized images of a 3D scene at arbitrary novel views from multi-view 2D images. Different from existing methods which apply stylization to trained neural radiance fields at the voxel level, our approach draws inspiration from image-to-painting methods, simulating the progressive painting process of human artwork with vector strokes. We develop a palette of stylized 3D strokes from basic primitives and splines, and consider the 3D scene stylization task as a multi-view reconstruction process based on these 3D stroke primitives. Instead of directly searching for the parame-ters of these 3D strokes, which would be too costly, we introduce a differentiable renderer that allows optimizing stroke parameters using gradient descent, and propose a training scheme to alleviate the vanishing gradient issue. The extensive evaluation demonstrates that our approach effectively synthesizes 3D scenes with significant geomet-ric and aesthetic stylization while maintaining a consis-tent appearance across different views. Our method can be further integrated with style loss and image-text con-trastive models to extend its applications, including color transfer and text-driven 3D scene drawing. Results and code are available at http://***/Neura13DStrokes.

关键词： Training Solid modeling Three-dimensional displays Stroke (medical condition) Neural radiance field Vectors Pattern recognition

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Opnet: Deep Occlusion Perception Network with Boundary Awareness for Amodal Instance Segmentation

Opnet: Deep Occlusion Perception Network with Boundary Aware...

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

作者： Shihui Zhang Ziteng Xue Yuhong Jiang Houlin Wang School of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The Amodal Instance Segmentation (AIS) task aims to infer the visible and occluded regions of an object instance. Existing AIS methods typically focus on directly predicting visible and occluded regions or leveraging prior knowledge to guide predictions. However, these methods often ignore the perception of occluded views, leading to inaccurate results. To address this issue and achieve high-quality AIS, we propose a boundary-aware Occlusion Perception Network (OPNet). OPNet consists of three main components: the Dynamic Feature Augmentation Pyramid (DFAP), the Dual-path Boundary Aware Module (DBAM), and the Shape-guide Refinement Module (SRM). Specifically, DBAM employs an occlusion-perception strategy to learn discriminative features with boundary information, enabling it to distinguish occlusion from multiple views. Additionally, DFAP and SRM optimize the results by enhancing feature aggregation and imposing geometric constraints. Experiments on the D2SA, KINS, and CWALT datasets show that OPNet significantly outperforms state-of-the-art AIS methods that without prior knowledge. Code is available at https://***/ZitengXue/OPNet.

关键词：

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TopicDVC: Dense Video Captioning with Topic Guidance

TopicDVC: Dense Video Captioning with Topic Guidance

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Edge Computing and Scalable Cloud (EdgeCom), IEEE International Conference on

作者： Wei Chen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350377132

ISBN: (纸本)9798350377149

Dense video captioning involves detecting and describing multiple events within a video coherently. Events within a video typically share a common topic, and incorporating this topic information into the model can enhance the quality and coherence of the generated captions. However, existing dense video captioning datasets lack explicit topic annotations. To address this, we design a topic generator that utilizes a diffusion model with strong generative capabilities to generate topic information from a given video. During the training stage, we use the features of ground-truth captions as pseudo-topic labels. The video topics diffuse from the features of groundtruth captions to a random distribution, and the model learns to reverse this noising process conditioned on video features. During inference, the model iteratively denoises the Gaussian noise into topic features conditioned on video features. In this paper, we propose TopicDVC, a novel dense video captioning framework. TopicDVC applies the topic information generated by the topic generator to guide the model in generating more coherent captions. Experiments on the ActivityNet Captions dataset demonstrate that leveraging the topics generated by the diffusion model significantly improves the performance of dense video captioning, producing more accurate and coherent captions.

关键词： Training Accuracy Annotations Video description Gaussian noise Coherence Diffusion models

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Audio-Driven Talking Face Video Generation with Emotion

Audio-Driven Talking Face Video Generation with Emotion

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

作者： Jiadong Liang Feng Lu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Vivid talking face generation has potential applications in virtual reality. Existing methods can generate talking faces that are synchronized with the audio, but typically ignore the accurate expression of emotions. In this paper, we propose an advanced two-step framework to synthesize talking face videos with vivid emotional appearances. The first step is designed to generate emotional fine-grained landmarks, including the normalized landmarks, gaze, and head pose. In the second step, we map the facial landmarks to latent key points, which are then fed into the pre-trained model to generate high-quality face images. Extensive experiments demonstrate the effectiveness of our method.

关键词： Solid modeling Three-dimensional displays Conferences virtual reality User interfaces Digital humans Synchronization

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