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9th International Conference on Digital Home, ICDH 2022

作者： Deng, Zhengjie Zhang, Bao He, Shuqian Wang, Yizhen Hainan Normal University School of Information Science and Technology Haikou China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Hainan Normal University School of Physics and Electronic Engineering Haikou China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

The rapid development of face forgery technology and the generation of realistic fake videos have caused serious harm to individuals, society and even the country, so it is important to detect deepfake videos. There are many detection methods available for forged videos, but the overall performance is yet to be improved and does not cope well with high quality forged images or videos. Observing that existing forgery algorithms leave synthetic forgery traces at the edges of faces when creating videos, this paper proposes a new method for detecting forged videos. It first finds the face edges from the video frames, then extracts the face edge bands as deep learning inputs and trains them based on EfficientNet-B3 to achieve effective detection of deepfake videos. Experiments show that the method in this paper can achieve more than 99.8% AUC values on all four forgery methods of the Face-Forensics++ dataset. © 2022 IEEE.

关键词： image enhancement

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A Deep Knowledge Tracking Model Based on Forgetting Features and Self-Attention Mechanism

A Deep Knowledge Tracking Model Based on Forgetting Features...

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Computer Engineering and intelligent Communications (ISCEIC), International Symposium on

作者： Guimin Huang Xiong Wei Qingkai Guo Nanxiao Deng Jianxing Lin School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

The integration of large education and artificial intelligence technologies is gradually deepening, and how to provide personalized user profiling services for learners is an important research problem. In response to the long-term dependency problem and lack of learning features of today’s deep knowledge tracking models, a deep knowledge tracking model (FSA-MuLSTM) based on forgetting features and the self-attentiveness mechanism is proposed. Firstly, the model combines the learner’s historical learning interaction tuple sequences with learning times and memory residuals to obtain the input vector based on the LSTM; secondly, the correlation weights between tuples are captured using the self-attention mechanism; finally, the deep information between tuple sequences is captured using the stacking of multilayer LSTMs. The probability of correctly answering the current question is derived from the hidden vectors and the self-attentive weights of the output of the multilayer LSTM. Two public datasets are used for the experiments, and the combined analysis outperforms existing knowledge-tracking models in terms of performance and improves the accuracy of prediction.

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Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

arXiv

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arXiv 2024年

作者： He, Fan He, Mingzhen Shi, Lei Huang, Xiaolin Suykens, Johan A.K. STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Ridgeless regression has garnered attention among researchers, particularly in light of the "Benign Overfitting" phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless regression does not always perform well due to the lack of flexibility. This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels, incorporating kernel learning techniques to improve performance in both experiments and theory. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit regularization in the proposed model, its optimization is equivalent to solving an 0-regularized problem in the integral space of RKHSs, elucidating the origin of its generalization ability. Taking an approximation analysis viewpoint, we introduce an lq-norm analysis technique (with 0 © 2024, CC BY.

关键词： Vector spaces

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Ya Zhou Ning Zhao Guimin Huang Nanxiao Deng Qingkai Guo Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.

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An Analysis Model of English Text Coherence Based on RST Dependency Relationship

An Analysis Model of English Text Coherence Based on RST Dep...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Guimin Huang Wanying Shuai Qingkai Guo Nanxiao Deng Jianxing Lin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively.

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A Method for Analyzing Teacher Behavior in Classroom Based on the Long- and Short-Term Features of Pose Sequences 9

A Method for Analyzing Teacher Behavior in Classroom Based o...

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9th International Conference on Digital Home, ICDH 2022

作者： Li, Yuanzhong Deng, Zhengjie Liu, Meijun He, Shuqian Wang, Yizhen Jiang, Wenjuan Hainan Normal University School of Information Science and Technology Haikou China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Hainan Normal University School of Physics and Electronic Engineering Haikou China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

The analysis of teachers' behaviors in classroom videos is helpful to objectively evaluate teaching, promote teaching reform, and improve teaching effectiveness. With the continuous introduction of deep learning models, there have been many analysis methods, but the recognition accuracy is not high enough. In this paper, we construct a new model combining convolutional neural network and recurrent neural network to learn and fuse short-term features with long-term features, and use the spatio-temporal information of videos to get the behavior categories. In our experiments, we collected teacher classroom videos in teaching scenarios to construct the dataset. The 2D pose heatmap sequences are extracted from the videos as model inputs, which are used to exclude the interference of environmental noise in the teaching scene. The experimental results show that this method can achieve higher recognition accuracy compared with the PoseR(2+1)D method on the teacher behavior dataset for teacher behavior analysis in teaching scenes. © 2022 IEEE.

关键词： Recurrent neural networks

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink

Research on Collaborative Filtering Algorithm of E-commerce ...

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International Conference on intelligent control and Information processing (ICICIP)

作者： Haohao Zheng Xiaonan Luo Xiaoshu Zhu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

In online shopping, a person’s interest in a product is closely related to whether they will purchase it Analyzing people’s interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user-item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the is of of matrix sparsity but its accuracy in recommending products based on implicit feedback alone is insufficient. This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by suimming the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product’s maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm’s accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

关键词： Accuracy Collaborative filtering Prediction algorithms Real-time systems Electronic commerce Sparse matrices User preference Matrix converters Root mean square intelligent control

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A English Pronunciation Error Detection and Quality Evaluation Model Based on English Text

A English Pronunciation Error Detection and Quality Evaluati...

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Computer Engineering and intelligent Communications (ISCEIC), International Symposium on

作者： Qingkai Guo Guimin Huang Jianxing Lin Ya Zhou School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

In English learning, speaking practice is crucial, but traditional classroom teaching can hardly meet the needs of most learners. In this paper, we investigate and improve the two essential techniques of pronunciation error detection and pronunciation quality evaluation in the Computer Assisted Language Learning (CALL) system. In terms of pronunciation error detection, We extracted phoneme pairs’ discriminative acoustic and phonological features. We used Support Vector Machine (SVM) for pronunciation error detection, and the accuracy was improved to 81.26%. For pronunciation quality evaluation, we added two new dimensions of pronunciation fluency and intonation, innovatively designed the word duration ratio, and introduced the Support Vector Regression (SVR) algorithm to achieve the effective integration of different dimensional features. The experiments show that the improved pronunciation quality evaluation model has higher performance and practical value.

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3D Cinemagraphy from a Single image

arXiv

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arXiv 2023年

作者： Li, Xingyi Cao, Zhiguo Sun, Huiqiang Zhang, Jianming Xian, Ke Lin, Guosheng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Adobe Research S-Lab Nanyang Technological University Singapore

We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inconsistent animation. Our key insight is that representing and animating the scene in 3D space offers a natural solution to this task. To this end, we first convert the input image into feature-based layered depth images using predicted depth values, followed by unprojecting them to a feature point cloud. To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow. Finally, to resolve the problem of hole emergence as points move forward, we propose to bidirectionally displace the point cloud as per the scene flow and synthesize novel views by separately projecting them into target image planes and blending the results. Extensive experiments demonstrate the effectiveness of our method. A user study is also conducted to validate the compelling rendering results of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Animation

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Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells

Constraining Depth Map Geometry for Multi-View Stereo: A Dua...

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International Conference on Computer Vision (ICCV)

作者： Xinyi Ye Weiyue Zhao Tianqi Liu Zihao Huang Zhiguo Cao Xin Li Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Albany Albany NY

Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation. Despite the excellent performance, existing methods ignore the fact that a suitable depth geometry is also critical in MVS. In this paper, we demonstrate that different depth geometries have significant performance gaps, even using the same depth prediction error. Therefore, we introduce an ideal depth geometry composed of Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward around the ground-truth surface, rather than maintaining a continuous and smooth depth plane. To achieve it, we develop a coarse-to-fine framework called Dual-MVSNet (DMVSNet), which can produce an oscillating depth plane. Technically, we predict two depth values for each pixel (Dual-Depth), and propose a novel loss function and a checkerboard-shaped selecting strategy to constrain the predicted depth geometry. Compared to existing methods, DMVSNet achieves a high rank on the DTU benchmark and obtains the top performance on challenging scenes of Tanks and Temples, demonstrating its strong performance and generalization ability. Our method also points to a new research direction for considering depth geometry in MVS.

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