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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yu Bai Ruian He Weimin Tan Bo Yan Yangle Lin School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Inpainting is a task to restore occlusion or other corruption on images. However, previous works require mask of the occluded area to restore the occluded image, which is inconvenient for application. Blind face inpainting aims to automatically restore the occluded face without position information of the corrupt region. In this paper, we propose a novel fine-grained blind face inpainting framework, combining 3D face components disentanglement with generative network. Canonical face texture and shape disentangled by unsupervised 3D face model is restored separately to get occlusion-free rendered result. Finally, the pixel-to-pixel generative module utilize the occlusion image and the coarse de-occlusion face to get refined inpainted result. We also build up a new dataset called CelebO-3D which consists of occluded face images synthesized with 3D occlusion and rendered by 3D face model. Extensive experiments show that the proposed method is effective and robust in face blind inpainting both in synthesized and real images. Extensive evaluations and comparison with previous methods also show our superior effectiveness and lightweight architecture.

关键词： Solid modeling Three-dimensional displays Shape Speech enhancement signal processing Acoustics Image restoration

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Broadband High Gain Composite VHF Monopole Antenna Design

Broadband High Gain Composite VHF Monopole Antenna Design

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Lihua Wang Baofeng Cao Peng Li Zongxiang Li Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China State Key Laboratory of Nuclear and Biochemical Calamity Protection for Civilian Beijing China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A novel wideband miniaturized composite monopole antenna is proposed, analysed and investigated in detail, where the antenna consists of a conical monopole structure with a loaded capacitive disc. The antenna is composed of multiple nested section, aiming to reduce the profile of the antenna. The loading disc at its top is equivalent to a capacitive structure to improve the antenna matching and the stability of the pattern. According to the simulation results, the impedance bandwidth $(\mathbf{S}_{\mathbf{11}} < -\mathbf{10})$ is 132.9% $(\mathbf{60.45}\sim \mathbf{300}\ \mathbf{MHz})$ , or even wider. The gain at the entire frequency averages 3 dBi. The performance of the antenna is very suitable for remote wireless communication.

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Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation

arXiv

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arXiv 2023年

作者： Cheng, Ri Jiang, Xuhao He, Ruian Zhou, Shili Tan, Weimin Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

The video frame interpolation (VFI) model applies the convolution operation to all locations, leading to redundant computations in regions with easy motion. We can use dynamic spatial pruning method to skip redundant computation, but this method cannot properly identify easy regions in VFI tasks without supervision. In this paper, we develop an Uncertainty-Guided Spatial Pruning (UGSP) architecture to skip redundant computation for efficient frame interpolation dynamically. Specifically, pixels with low uncertainty indicate easy regions, where the calculation can be reduced without bringing undesirable visual results. Therefore, we utilize uncertainty-generated mask labels to guide our UGSP in properly locating the easy region. Furthermore, we propose a self-contrast training strategy that leverages an auxiliary non-pruning branch to improve the performance of our UGSP. Extensive experiments show that UGSP maintains performance but reduces FLOPs by 34%/52%/30% compared to baseline without pruning on Vimeo90K/UCF101/MiddleBury datasets. In addition, our method achieves state-of-the-art performance with lower FLOPs on multiple benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior

arXiv

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arXiv 2023年

作者： Zhou, Shili Jiang, Xuhao Tan, Weimin He, Ruian Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

In recent years, many deep learning-based methods have been proposed to tackle the problem of optical flow estimation and achieved promising results. However, they hardly consider that most videos are compressed and thus ignore the pre-computed information in compressed video streams. Motion vectors, one of the compression information, record the motion of the video frames. They can be directly extracted from the compression code stream without computational cost and serve as a solid prior for optical flow estimation. Therefore, we propose an optical flow model, MVFlow, which uses motion vectors to improve the speed and accuracy of optical flow estimation for compressed videos. In detail, MVFlow includes a key Motion-Vector Converting Module, which ensures that the motion vectors can be transformed into the same domain of optical flow and then be utilized fully by the flow estimation module. Meanwhile, we construct four optical flow datasets for compressed videos containing frames and motion vectors in pairs. The experimental results demonstrate the superiority of our proposed MVFlow, which can reduce the AEPE by 1.09 compared to existing models or save 52% time to achieve similar accuracy to existing models. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical flows

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A Multi-level Approach to Learning Early Warning based on Cognitive Diagnosis and Learning Behaviors Analysis 26

A Multi-level Approach to Learning Early Warning based on Co...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ma, Hua Zhao, Wen Jiang, Zixu Huang, Peiji Tang, Wensheng Zhang, Hongyu Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan Normal University College of Information Science and Engineering Changsha China Co. Ltd China Central South University School of Business Changsha China

ISBN: (纸本)9798350331684

Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately. © 2023 IEEE.

关键词： Students

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Optimized Gradient Clipping for Noisy Label Learning 39

Optimized Gradient Clipping for Noisy Label Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ye, Xichen Wu, Yifan Zhang, Weizhong Li, Xiaoqiang Chen, Yifan Jin, Cheng Shanghai University China Fudan University China Hong Kong Baptist University Hong Kong Shanghai Key Laboratory of Intelligent Information Processing China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)157735897X

Previous research has shown that constraining the gradient of loss function w.r.t. model-predicted probabilities can enhance the model robustness against noisy labels. These methods typically specify a fixed optimal threshold for gradient clipping through validation data to obtain the desired robustness against noise. However, this common practice overlooks the dynamic distribution of gradients from both clean and noisy-labeled samples at different stages of training, significantly limiting the model capability to adapt to the variable nature of gradients throughout the training process. To address this issue, we propose a simple yet effective approach called Optimized Gradient Clipping (OGC), which dynamically adjusts the clipping threshold based on the ratio of noise gradients to clean gradients after clipping, estimated by modeling the distributions of clean and noisy samples. This approach allows us to modify the clipping threshold at each training step, effectively controlling the influence of noise gradients. Additionally, we provide statistical analysis to certify the noise-tolerance ability of OGC. Our extensive experiments across various types of label noise, including symmetric, asymmetric, instance-dependent, and real-world noise, demonstrate the effectiveness of our approach. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Super-Resolution of Defocus Thread Image Based on Cycle Generative Adversarial Networks 12th

Super-Resolution of Defocus Thread Image Based on Cycle Gene...

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12th IFIP TC 12 International Conference on intelligent Information processing, IIP 2022

作者： Jiang, Pengfei Xu, Wangqing Li, Jinping School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-Year Jinan250022 China

ISBN: (纸本)9783031039478

The dual camera calibration measurement method can realize low-cost and high-precision bolt dimension measurement by using two microscope cameras. But the height difference between the thread crest and root exceeds the depth of field, and the thread image becomes defocus, which seriously affects the measurement accuracy. For this reason, a super-resolution method for defocus thread image based on cyclic generative adversarial networks is proposed. We collected focus thread images and defocus thread images as training data. Two encoders are used in the generation network to extract image defocus features and content features. And a sub-pixel convolution layer is added to the decoder to achieve image super-resolution. A loss function based on adversarial loss and cycle-consistent loss is constructed to realize unsupervised training of the network, thereby achieve super-resolution of defocus thread images. The experimental results show that, in the simulated defocus images, the method has superiority in image detail preservation, sharpness improvement and peak signal to noise ratio. In the bolt dimension measurement task, it can effectively reconstruct the clear thread image and thus provide the measurement accuracy to 0.01 mm. © 2022, IFIP International Federation for Information processing.

关键词： Optical resolving power

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Hierarchical Clustering Combination Method Using Intuitionistic Fuzzy Similarity in Software Module Clustering

Hierarchical Clustering Combination Method Using Intuitionis...

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International Conference on Networking and Network Applications (NaNA)

作者： Hong Xia Bo Wang Yongkang Zhang Yanping Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi'an Shaanxi China

In order to solve the problem that the similarity method used in software module clustering can produce arbitrary decision, and the description matrix of dendrogram generated by base clustering in hierarchical clustering combination does not consider more characteristics of entities, a hierarchical clustering combination method based on intuitionistic fuzzy similarity (HCC-IFS) was proposed. In the process of entity similarity calculation, the intuitionistic fuzzy set theory and similarity calculation method are used to provide membership and non-membership attributes for each entity, so as to improve the merging efficiency and solve arbitrary decision problems. In the description matrix representation of base clustering, the absolute and relative features obtained from the dendrogram are used to construct the description matrix, and the generated description matrix is combined. Finally, the proposed algorithm is verified by experiments. Experimental results show that HCC-IFS can produce better clustering results and improve the recovery quality of software architecture.

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Spectral Galerkin Approximation of Fractional Optimal Control Problems with Fractional Laplacian

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Advances in Applied Mathematics and Mechanics 2023年第6期15卷 1631-1654页

作者： Jiaqi Zhang Yin Yang Zhaojie Zhou School of Mathematics and Computational Science Xiangtan UniversityHunan Key Laboratory for Computation and Simulation in Science and EngineeringKey Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan 411105HunanChina School of Mathematics and Computational Science Xiangtan UniversityNational Center for Applied Mathematics in HunanHunan International Scientific and Technological Innovation Cooperation Base of Computational ScienceXiangtan 411105HunanChina School of Mathematics and Statistics Shandong Normal UniversityJinan 250014ShandongChina

In this paper spectral Galerkin approximation of optimal control problem governed by fractional elliptic equation is *** deal with the nonlocality of fractional Laplacian operator the Caffarelli-Silvestre extension is *** first order optimality condition of the extended optimal control problem is derived.A spectral Galerkin discrete scheme for the extended problem based on weighted Laguerre polynomials is developed.A priori error estimates for the spectral Galerkin discrete scheme is *** experiments are presented to show the effectiveness of our methods and to verify the theoretical findings.

关键词： Fractional Laplacian optimal control problem Caffarelli-Silvestre extension weighted Laguerre polynomials

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