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A New Head Pose Estimation Method Using Vision Transformer Model 2021

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A New Head Pose Estimation Method Using Vision Transformer M...

7th International Conference on Computing and Artificial Intelligence, ICCAI 2021

作者： Ling, Xufeng Wang, Dong Yang, Jie Shanghai Normal University Tianhua College Ai School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Normal University Tianhua College China

ISBN: (纸本)9781450389501

In this paper, a self-attention-based Vision Transformer (VIT) method is introduced into estimate human head pose parameters. Firstly, the head pose image is divided into 32X32 patches, each image patch is regarded as a word, and the whole image is treated as a paragraph composed of n words by the VIT. image recognition can be regarded as the semantic recognition of this paragraph. Next, we redesign the regression VIT to estimate the parameters. Then we select Head Pose Database as the training and validation dataset. The VIT is trained on the enhanced and normalized dataset. Finally, the trained VIT is used to regress the head pose parameters on testing samples. Experimental results show that VIT has high accuracy and good generalization ability for head pose estimation. © 2021 ACM.

关键词： image recognition

CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene

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arXiv 2022年

作者： Wei, Pengjin Yan, Guohang Li, Yikang Fang, Kun Cai, Xinyu Yang, Jie Liu, Wei The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China The Autonomous Driving Group Shanghai AI Laboratory China

Sensor-based environmental perception is a crucial part of the autonomous driving system. In order to get an excellent perception of the surrounding environment, an intelligent system would configure multiple LiDARs (3D Light Detection and Ranging) to cover the distant and near space of the car. The precision of perception relies on the quality of sensor calibration. This research aims at developing an accurate, automatic, and robust calibration strategy for multiple LiDAR systems in the general road scene. We thus propose CROON (automatic multi-LiDAR Calibration and Refinement methOd in rOad sceNe), a two-stage method including rough and refinement calibration. The first stage can calibrate the sensor from an arbitrary initial pose, and the second stage is able to precisely calibrate the sensor iteratively. Specifically, CROON utilize the nature characteristics of road scene so that it is independent and easy to apply in large-scale conditions. Experimental results on real-world and simulated data sets demonstrate the reliability and accuracy of our method. All the related data sets and codes are open-sourced on the Github website https://***/OpenCalib/LiDAR2LiDAR. Copyright © 2022, The Authors. All rights reserved.

关键词： Intelligent systems

Consensus-Based Distributed Algorithm for Gep

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SSRN

SSRN 2022年

作者： Lv, Kexin He, Fan Huang, Xiaolin Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China

Generalized eigenvalue problem (GEP) plays a significant role in signal processing and machine learning. This paper proposes a consensus-based distributed algorithm for GEP in multi-agent systems, where data samples are distributively stored across agents. The distributed GEP is reformulated as a consensus optimization, but the presence of its quadratic inseparable constraint makes the considered problem more challenging. To deal with it, a sequential method is proposed combined with the alternating direction method of multipliers, which requires communication between couples of nodes. Theoretical analysis shows the proposed algorithm will converge to the set of stationary solutions. And the numerical experiments on synthetic and real-world datasets validate that the approximated solution is competitive to the ground truth. © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

Mushroom Classification Based on Deep Residual Network

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Mushroom Classification Based on Deep Residual Network

pattern recognition and Machine Learning (PRML), IEEE International Conference on

作者： Ju Feng Xufeng Ling Yubo Wang Jie Yang School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Institute of Image Processing and Pattern Recognition and Institute of Medical Robotics Shanghai Jiaotong University Shanghai China

Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. Then, a transfer learning strategy is employed to initialize the network parameters from a pre-trained model, followed by fine-tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. The experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. This method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health.

关键词：

MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation

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arXiv 2023年

作者： Tang, Fenghe Nian, Bingkun Ding, Jianrui Quan, Quan Yang, Jie Liu, Wei Zhou, S. Kevin School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Computer Science and Technology Harbin Institute of Technology China Institute of Computing Technology China

Due to the scarcity and specific imaging characteristics in medical images, light-weighting Vision Transformers (ViTs) for efficient medical image segmentation is a significant challenge, and current studies have not yet paid attention to this issue. This work revisits the relationship between CNNs and Transformers in lightweight universal networks for medical image segmentation, aiming to integrate the advantages of both worlds at the infrastructure design level. In order to leverage the inductive bias inherent in CNNs, we abstract a Transformer-like lightweight CNNs block (ConvUtr) as the patch embeddings of ViTs, feeding Transformer with denoised, non-redundant and highly condensed semantic information. Moreover, an adaptive Local-Global-Local (LGL) block is introduced to facilitate efficient local-to-global information flow exchange, maximizing Transformer's global context information extraction capabilities. Finally, we build an efficient medical image segmentation model (MobileUtr) based on CNN and Transformer. Extensive experiments on five public medical image datasets with three different modalities demonstrate the superiority of MobileUtr over the state-of-the-art methods, while boasting lighter weights and lower computational cost. Code is available at https://***/FengheTan9/MobileUtr. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature

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arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven LeuvenB-3001 Belgium

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the random features on different nodes are identical. However, in many applications, data on different nodes varies significantly on the number or distribution, which calls for adaptive and data-dependent methods that generate different RFs. To tackle the essential difficulty, we propose a new decentralized KRR algorithm that pursues consensus on decision functions, which allows great flexibility and well adapts data on nodes. The convergence is rigorously given and the effectiveness is numerically verified: by capturing the characteristics of the data on each node, while maintaining the same communication costs as other methods, we achieved an average regression accuracy improvement of 25.5% across six real-world data sets. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method

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arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Wang, Kaijie Huang, Xiaolin Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University 800 Dongchuan RD Shanghai200240 China STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Oude Markt 13 Leuven3000 Belgium

Data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputation is helpful for learning performance, and it follows that the pursuit of better classification can guide the data imputation process. While some works consider using label information to assist in this task, their simplistic utilization of labels lacks flexibility and may rely on strict assumptions. In this paper, we propose a new framework that effectively leverages supervision information to complete missing data in a manner conducive to classification. Specifically, this framework operates in two stages. Firstly, it leverages labels to supervise the optimization of similarity relationships among data, represented by the kernel matrix, with the goal of enhancing classification accuracy. To mitigate overfitting that may occur during this process, a perturbation variable is introduced to improve the robustness of the framework. Secondly, the learned kernel matrix serves as additional supervision information to guide data imputation through regression, utilizing the block coordinate descent method. The superiority of the proposed method is evaluated on four real-world data sets by comparing it with state-of-the-art imputation methods. Remarkably, our algorithm significantly outperforms other methods when the data is missing more than 60% of the features. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

Supervised Classification of Plant image Based on Attention Mechanism

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Supervised Classification of Plant Image Based on Attention ...

International Conference on Systems and Informatics (ICSAI)

作者： Jie Li Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665426251

In view of the wide variety of plants on the earth, the plant species identification is particularly necessary to protect and preserve biodiversity. In this work, we propose a plant image classification method based on the encoder-decoder model with additive attention mechanism to extract plant image features and convert them into text descriptions related to plant features. In a well-trained network, it can successfully classify on the species of the generated plant texts. We show that, the proposed method not only equalizes the results of deep convolutional neural network on classification task, but also uses of the prior information of botanists in classification, and thus provide a significant prediction result.

关键词： Earth image recognition Text recognition Biological system modeling Feature extraction Convolutional neural networks Biodiversity

Indoor Elderly Fall recognition System Based on Target Detection and Self-attention Model 2

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Indoor Elderly Fall Recognition System Based on Target Detec...

2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021

作者： Liu, Min Ling, Xufeng Yang, Jie School of Health Tianhua College Shanghai Normal University Shanghai China AI School Tianhua College Shanghai Normal University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781665437578

With the increasing aging of the society, a series of consequences caused by falls of the elderly have become a serious medical problem and a real social problem. Aiming at the fall behavior of the elderly, the research and development of human fall recognition technology has practical application value. This paper proposes a method to recognize human targets in indoor images using target detection technology, and then use the trained neural network based on Self-attention technique named Vision Transformer (ViT) to recognize the falling posture of the elderly. Firstly, the target detection model is optimized and trained for indoor scene, so that the model can accurately detect human targets in indoor scene;Next, the indoor scene human target image is preprocessed, and the indoor human pose image data set is constructed;Then, the data set is used to train the ViT depth network, and enable it to correctly classify five postures (standing, sitting, lying, bending and crawling). The experimental results show that ViT network achieves high accuracy and excellent generalization capacity for the above 5 human posture and can realize the recognition of elderly falls. © 2021 IEEE.

关键词： Deep learning