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Augmented Feature Representation with Parallel Convolution for Cross-domain Facial Expression Recognition 16th

Augmented Feature Representation with Parallel Convolution ...

引用

16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Yang, Fan Xie, Weicheng Zhong, Tao Hu, Jingyu Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (纸本)9783031202322

Facial expression recognition (FER) has made significant progress in the past decade, but the inconsistency of distribution between different datasets greatly limits the generalization performance of a learned model on unseen datasets. Recent works resort to aligning feature distributions between domains to improve the cross-domain recognition performance. However, current algorithms use one output each layer for the feature representation, which can not well represent the complex correlation among multi-scale features. To this end, this work proposes a parallel convolution to augment the representation ability of each layer, and introduces an orthogonal regularization to make each convolution represent independent semantic. With the assistance of a self-attention mechanism, the proposed algorithm can generate multiple combinations of multi-scale features to allow the network to better capture the correlation among the outputs of different layers. The proposed algorithm achieves state-of-the-art (SOTA) performances in terms of the average generalization performance on the task of cross-database (CD)-FER. Meanwhile, when AFED or RAF-DB is used for the training, and other four databases, i.e. JAFFE, SFEW, FER2013 and EXPW are used for testing, the proposed algorithm outperforms the baselines by the margins of 5.93% and 2.24% in terms of the average accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs 37

AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Ve...

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Cobb, Adam D. Roy, Anirban Elenius, Daniel Heim, F. Michael Swenson, Brian Whittington, Sydney Walker, James D. Bapty, Theodore Hite, Joseph Ramani, Karthik McComb, Christopher Jha, Susmit Computer Science Laboratory SRI International United States Mechanical Engineering Division Southwest Research Institute United States Intelligent Systems Division Southwest Research Institute United States Institute of Software Integrated Systems Vanderbilt University United States School of Mechanical Engineering Purdue University United States Department of Mechanical Engineering Carnegie Mellon University United States

ISBN: (纸本)9781713899921

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27, 714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details;a STandard for the Exchange of Product (STEP) model data;a 3D CAD design using a stereolithography (STL) file format;a 3D point cloud for the shape of the design;and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446, baseline models and code at https://***/SRI-CSL/AircraftVerse, and the dataset description at https://***/. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Computer aided logic design

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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An End-to-End Training Method to Ensemble Bi-Linear Models for Knowledge Graph Completion

An End-to-End Training Method to Ensemble Bi-Linear Models f...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Si Cen Han Liu Chao Liu Xiaoying Zou Guoquan Dai Xizhao Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

The study of ensemble learning in knowledge graph embedding (KGE) shows that combining multiple individual KGE models can perform better on knowledge graph completion. However, existing KGE ensemble methods ignore the creation of model diversity because these methods independently train individual models, which are short of training interaction. To create rich model diversity, we propose a novel training method for ensemble bilinear models (EBM) for the problem of knowledge graph completion. EBM uses a weighted loss to allow individual KGE models to interact during training. In this way, the relations in the knowledge graph can be automatically modeled by the most appropriate model from the KGE individual ones. The experiments on knowledge graph completion show that EBM has richer diversity and performs better than the single bilinear model and the ensemble methods without interaction.

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A hybrid equilibrium optimizer with differential evolution algorithm

A hybrid equilibrium optimizer with differential evolution a...

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Computer, Internet of Things and Smart City (CIoTSC), International Conference on

作者： Meijun Duan Chao Zhang Bo Li School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China

ISBN: (数字)9798350306569

ISBN: (纸本)9798350306576

For the population diversity decreasing rapidly in differential evolution (DE), a novel hybrid algorithm based on Equilibrium Optimizer (EO) and DE (HEODE) is presented. Firstly, a sharing learning mutation strategy is introduced in DE based on the sharing individual. By learning from the sharing individual, information exchange among the whole population can be achieved. Secondly, an effective hybridization mechanism is proposed to combine the advantages of two algorithms. A serial of experiments are used for effectiveness verification among 12 DEs and 5 none-DE based algorithms. The test results validate that HEODE outperforms all competitors.

关键词： Sequential analysis Smart cities Path planning Internet of Things Aircraft Optimization Information exchange Electo-optic effects Convergence

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A Review of Deep Learning-Based Super-Resolution 20

A Review of Deep Learning-Based Super-Resolution

引用

20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Weiqin, Chen Bo, Chen Yuandan, Deng Yi, Tian Yanling, Mao Juntao, Zeng Sichuan Provincial Key Laboratory of Intelligent Terminal Jointly Built by Hall and City Yi Bin644000 China University of Electronic Science and Technology of China Yibin Park Yi Bin644000 China School of Information and Software Engineering University of Electronic Science and Technology of China Cheng Du610000 China

ISBN: (纸本)9798350318982

Super-resolution technology generally refers to the use of known information to infer more vivid and detailed images. Due to their wide range of applications, they have consistently garnered significant attention and discussion in the field of image vision. This paper aims to give a comprehensive overview of the super-resolution reconstruction technology under the framework of deep learning, divide it into five categories according to the network model and expand the overview. In addition, it also organizes the commonly used data sets and evaluation indicators for training super-resolution algorithms, and summarizes the influence of data sets on these algorithms. Finally, this paper points out the challenges and limitations of the current super-resolution technology, and looks forward to the future. © 2023 IEEE.

关键词： Deep learning

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

引用

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

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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Low-Sampling-Frequency Plane wave Medical Ultrasound Imaging based on Adversarial Learning

Low-Sampling-Frequency Plane wave Medical Ultrasound Imaging...

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IEEE International Conference on Image Processing

作者： Xiaoteng Chen Junying Chen School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education China

In plane wave medical ultrasound imaging, the analog echo signals are received via the transducer, and sampled as digital radio frequency data which is further processed by the beamforming algorithm to obtain the output ultrasound image. The sampling frequency of radio frequency data has a great impact on the image quality. The radio frequency data with higher sampling frequency is closer to the true distribution of the analog echo signals, leading to better image quality. However, high-frequency sampling chips are usually expensive. Thus, improving the plane wave imaging quality for radio frequency data with low sampling frequency has important practical significance. The ultrasound imaging quality is mainly affected by shallow features such as texture and color. However, low-sampling-frequency data has poor shallow features while high-sampling-frequency data has better shallow features. In this work, we propose an adversarial learning method to replace the shallow features of low-frequency data with the shallow features of high-frequency data, but retaining the deep features of low-frequency data. The experiment results demonstrate that the proposed method improves the plane wave imaging quality for low-sampling-frequency data by fusing shallow and deep features.

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Deep Reinforcement Learning with Human Guidance for Autonomous Driving Car Following

Deep Reinforcement Learning with Human Guidance for Autonomo...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Wenbo Wang Qingchao Xu Xingfu Yang Yandong Liu School of Software Engineering Dalian University Dalian China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

To solve the complex modeling and difficult parameter adjustment of traditional car-following control methods, we propose the car-following framework KgPPO-CF. The KgPPOCF brings expert experience into the interactive process of Deep Reinforcement Learning, which improves the speed and robustness of training policies. We use four different reward functions and compare the effects of policies trained by different designs on car-following control. In the Carla simulator, KgPPO-CF achieves car-following control in a continuous action space. The experimental results show that, compared with the traditional control methods, the control strategy obtained by KgPPO-CF training has better adaptability.

关键词： Training Accuracy Mechatronics Aerospace electronics Deep reinforcement learning Robustness Automobiles Robotics and automation Standards Autonomous vehicles

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Identifying Code Changes for Architecture Decay via a Metric Forest Structure

Identifying Code Changes for Architecture Decay via a Metric...

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IEEE/ACM International Conference on Technical Debt (TechDebt)

作者： Wuxia Jin Yuyun Zhang Jiaowei Shang Yi Hou Ming Fan Ting Liu School of Software Engineering Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University

During long-term software evolution, it is inevitable that an accumulation of changes leads to architectural erosion and debt. Diverse metric-based methods have been developed to identify architectural problems that violate design principles and degrade software maintainability. However, there still exists a gap between the implementation-level metrics and architecture-level metrics. Consequently, it hinders the comprehensibility, interpretability, and indicative(-bility) of the measurement results. To fill this gap, we propose the dbMIT to identify potential code changes that make architecture decay. Our dbMIT first integrates popular metrics such as the CK suite. Then dbMIT constructs a forest structure of metrics, serving as a knowledge base to relate the metrics across levels together. The forest aims to link the architecture-level metrics and implementation-level metrics. Via pre-defined rules using the forest structure, our dbMIT identifies code changes that potentially cause the architecture decay. The usage of forest structure of metrics makes it easy for developers to understand detection results, explain why the detected code changes are potential contributors to the decay, and indicate the code scope for resolution. We also contribute a web-based tool to integrate our dbMIT. Our experiments on the collected projects demonstrate the effectiveness of dbMIT against a history-based ground-truth.

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