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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot 6

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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

6th International Conference on robotics and Automation Engineering, ICRAE 2021

作者： Chengbo, Li Xiaoming, Wang Zixi, Huang Guangyao, Pang Xinyu, Han Yaguang, Lin Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an710119 China Shenzhen Middle School Shenzhen China Wuzhou University School of Data Science and Software Engineering Wuzhou China Singapore Polytechnic Advanced Robotics and Intelligent Control Centre Singapore

ISBN: (纸本)9781665406970

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the 'Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm'. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot. © 2021 IEEE.

关键词： Earthquakes

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

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arXiv 2024年

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

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 will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation

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PoseFormerV2: Exploring Frequency Domain for Efficient and R...

Conference on computer vision and Pattern Recognition (CVPR)

作者： Qitao Zhao Ce Zheng Mengyuan Liu Pichao Wang Chen Chen Shandong University Key Laboratory of Machine Perception Peking University Shenzhen Graduate School Amazon Prime Video Center for Research in Computer Vision University of Central Florida

Recently, transformer-based methods have gained significant success in sequential 2D-to-3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial relations of human joints in each video frame and human dynamics across frames with cascaded transformer layers and has achieved impressive performance. However, in real scenarios, the performance of PoseFormer and its follow-ups is limited by two factors: (a) The length of the input joint sequence; (b) The quality of 2D joint detection. Existing methods typically apply self-attention to all frames of the input sequence, causing a huge computational burden when the frame number is increased to obtain advanced estimation accuracy, and they are not robust to noise naturally brought by the limited capability of 2D joint detectors. In this paper, we propose PoseFormerV2, which exploits a compact representation of lengthy skeleton sequences in the frequency domain to efficiently scale up the receptive field and boost robustness to noisy 2D joint detection. With minimum modifications to PoseFormer, the proposed method effectively fuses features both in the time domain and frequency domain, enjoying a better speed-accuracy trade-off than its precursor. Extensive experiments on two benchmark datasets (i.e., Human3.6M and MPI-INF-3DHP) demonstrate that the proposed approach significantly outperforms the original PoseFormer and other transformer-based variants. Code is released at https://***/ QitaoZhao/PoseFormerV2.

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Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation

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arXiv 2020年

作者： Zhou, Yanning Chen, Hao Lin, Huangjing Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Deep learning methods show promising results for overlapping cervical cell instance segmentation. However, in order to train a model with good generalization ability, voluminous pixel-level annotations are demanded which is quite expensive and time-consuming for acquisition. In this paper, we propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining (MMT-PSM), which consists of a teacher and a student network during training. Two networks are encouraged to be consistent both in feature and semantic level under small perturbations. The teacher’s self-ensemble predictions from K-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student. We design a novel strategy to estimate the sensitivity to perturbations for each proposal and select informative samples from massive cases to facilitate fast and effective semantic distillation. In addition, to eliminate the unavoidable noise from the background region, we propose to use the predicted segmentation mask as guidance to enforce the feature distillation in the foreground region. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only, and outperforms state-of-the-art semi-supervised methods. Code: https://***/SIAAAAAA/MMT-PSM. Copyright © 2020, The Authors. All rights reserved.

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Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization

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arXiv 2020年

作者： Wang, Shujun Li, Caizi Heng, Pheng-Ann Fu, Chi-Wing Yu, Lequan Chinese University of Hong Kong Hong Kong Stanford University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

The generalization capability of neural networks across domains is crucial for real-world applications. We argue that a generalized object recognition system should well understand the relationships among different images and also the images themselves at the same time. To this end, we present a new domain generalization framework (called EISNet) that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains. To be specific, we formulate our framework with feature embedding using a multi-task learning paradigm. Besides conducting the common supervised recognition task, we seamlessly integrate a momentum metric learning task and a self-supervised auxiliary task to collectively integrate the extrinsic and intrinsic supervisions. Also, we develop an effective momentum metric learning scheme with the K-hard negative mining to boost the network generalization ability. We demonstrate the effectiveness of our approach on two standard object recognition benchmarks VLCS and PACS, and show that our EISNet achieves state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

Trajectory Tracking of Soft Continuum Robots with Unknown Models Based on Varying Parameter Recurrent Neural Networks

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Trajectory Tracking of Soft Continuum Robots with Unknown Mo...

IEEE International Conference on Systems, Man and Cybernetics

作者： Ning Tan Peng Yu Fenglei Ni Zhenglong Sun School of Computer Science and Engineering Sun Yat-sen University Guangzhou PR China State Key Laboratory of Robotics and Systems (HIT) Harbin PR China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9781665442084

Bio-inspired robots, e.g., soft continuum robots, have broad application prospects due to their structural dexterity and interaction safety. But these features also bring great challenges to the precise control of soft continuum robots. In this work, we investigate how to achieve the kinematic control of soft continuum robots without knowing model parameters of the robots. To this end, a model-free scheme based on varying-parameter recurrent neural networks (VP-RNN) is proposed. The scheme involves two components, one of which solves the inverse kinematics problem based on a VP-RNN model, and the other employs another VP-RNN model to estimate the pseudo-inverse of Jacobian matrix of continuum robots. Finally, the feasibility and robustness of the proposed control strategy are validated by simulations, including comparisons with other methods and case study with jammed actuation.

关键词： Jacobian matrices Recurrent neural networks Trajectory tracking Prototypes Kinematics Interference Robustness

Multiple Autoencoder-Based Anomaly Detection with Fuzzy Rough Sets

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SSRN

SSRN 2024年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Yue, Xiaodong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Anomaly detection is a practical and essential research topic with a wide range of applications. However, existing anomaly detection methods may face challenges when handling high-dimensional data with complex distributions. In this study, we propose a multiple autoencoder-based anomaly detection method with the aid of fuzzy rough sets. Specifically, the autoencoder is first improved by introducing the kernel fuzzy relation to enhance its representation capability in low-dimensional space. Then, the theory of fuzzy rough sets is employed to perform anomaly detection in the learned low-dimensional representation by fusing multi-view proximity-based information. Finally, to handle complex data, multiple autoencoders are utilized to collaboratively detect anomalies by integrating local anomaly information from different perspectives. Comparative experiments conducted on the selected datasets reveal that the proposed method is superior to state-of-the-art methods, improving over classical autoencoder by 5.58% in terms of the AUC-ROC index. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

A Uniform Representation Learning Method for OCT-based Fingerprint Presentation Attack Detection and Reconstruction

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arXiv 2022年

作者： Zhang, Wentian Liu, Haozhe Liu, Feng Ramachandra, Raghavendra The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Gjøvik2818 Norway

The technology of optical coherence tomography (OCT) to fingerprint imaging opens up a new research potential for fingerprint recognition owing to its ability to capture depth information of the skin layers. Developing robust and high security Automated Fingerprint Recognition Systems (AFRSs) are possible if the depth information can be fully utilized. However, in existing studies, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction based on depth information are treated as two independent branches, resulting in high computation and complexity of AFRS ***, this paper proposes a uniform representation model for OCT-based fingerprint PAD and subsurface fingerprint reconstruction. Firstly, we design a novel semantic segmentation network which only trained by real finger slices of OCT-based fingerprints to extract multiple subsurface structures from those slices (also known as B-scans). The latent codes derived from the network are directly used to effectively detect the PA since they contain abundant subsurface biological information, which is independent with PA materials and has strong robustness for unknown PAs. Meanwhile, the segmented subsurface structures are adopted to reconstruct multiple subsurface 2D fingerprints. Recognition can be easily achieved by using existing mature technologies based on traditional 2D fingerprints. Extensive experiments are carried on our own established database, which is the largest public OCT-based fingerprint database with 2449 volumes. In PAD task, our method can improve 0.33% Acc from the state-of-the-art method. For reconstruction performance, our method achieves the best performance with 0.834 mean Intersection of Union (mIOU) and 0.937 Pixel Accuracy (PA). By comparing with the recognition performance on surface 2D fingerprints (e.g. commercial and high resolution), the effectiveness of our proposed method on high quality subsurface fingerprint reconstruction is further proved. Copyright ©

关键词： Optical tomography

Mask-ShadowGAN: Learning to Remove Shadows From Unpaired Data

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Mask-ShadowGAN: Learning to Remove Shadows From Unpaired Dat...

International Conference on computer vision (ICCV)

作者： Xiaowei Hu Yitong Jiang Chi-Wing Fu Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employing adversarial learning and cycle-consistency constraints is insufficient to learn the underlying relationship between the shadow and shadow-free domains, since the mapping between shadow and shadow-free images is not simply one-to-one. To address the problem, we formulate Mask-ShadowGAN, a new deep framework that automatically learns to produce a shadow mask from the input shadow image and then takes the mask to guide the shadow generation via re-formulated cycle-consistency constraints. Particularly, the framework simultaneously learns to produce shadow masks and learns to remove shadows, to maximize the overall performance. Also, we prepared an unpaired dataset for shadow removal and demonstrated the effectiveness of Mask-ShadowGAN on various experiments, even it was trained on unpaired data.

关键词： Generators Training Manganese Training data Image color analysis Shape Cameras