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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhida Feng Li Chen Jing Tian JiaXiang Liu Shikun Feng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Baidu Inc. Institute of Systems Science National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduced StyleEntity, a zero-shot image manipulation model that utilizes named entities as proxies during its training phase. This strategy enables our model to manipulate images using unseen textual descriptions during inference, all within a single training phase. Additionally, we proposed an inference technique termed Prompt Ensemble Latent Averaging (PELA). PELA averages the manipulation directions derived from various named entities during inference, effectively eliminating the noise directions, thus achieving stable manipulation. In our experiments, StyleEntity exhibited superior performance in a zero-shot setting compared to other methods. The code, model weights, and datasets are available at https://***/feng-zhida/StyleEntity.

关键词： Training Adaptation models Computer vision Codes Computational modeling Noise Stability analysis

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RISC-VTF: RISC-V Based Extended Instruction Set for Transformer

RISC-VTF: RISC-V Based Extended Instruction Set for Transfor...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qiang Jiao Wei Hu Fang Liu Yong Dong College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China School of Computer Science Wuhan University Wuhan China Wuhan City College Wuhan China

ISBN: (纸本)9781665442084

Deep learning model Transformer has been widely used in natural language processing(NLP) filed, and its demand for computing resources is also growing. However, general-purpose processors(CPU and GPU) have invested excessive hardware resources in the design because they have to flexibly support a variety of tasks, they are not efficient for the implementation of Transformer. Consequently, various software optimizations that towards general-purpose processors have been proposed one after another. But, under the condition of ensuring sufficient accuracy, the degree of software optimization is limited. It is requisite to friendly-support Transformer at the hardware *** analysing the computational characteristics of the Transformer model, based on RISC-V, we designed a hardware friendly instruction set architecture for the Transformer model. In addition to the basic instruction, for the intensive and general computing part of the model, according to the expansion rules of RISC-V instruction, we design the matrix load/store instruction calculation instruction, softmax instruction, activation instruction and other user-defined instructions. They support any matrix scale, and deploy it on FPGA to realize a flexible and efficient custom processor RISC-VTF for Transformer. The design is integrated on the Xilinx toolkit zynq-7000 FPGA, and the resource consumption and performance are analyzed. Compared with the traditional common ISA(Instruction Set Architecture) such as x86, arm or MIPs, RISC-VTF provides higher code density and performance efficiency.

关键词： Analytical models Computational modeling Instruction sets Computer architecture Transformers Hardware Software

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Radar range Extended target detection using Prototypical Network

Radar range Extended target detection using Prototypical Net...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Wenhao Ma Mingchen Yuan Xinyang Wang Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou China Xi'an Modern Control Technology Research Institute Xi'an China Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded•Real-Time Information: Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB.

关键词： Road transportation Radar detection Object detection Radar Soil Probability Feature extraction Classification algorithms Clutter Signal to noise ratio

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Video-Based 3D Human Pose Estimation Research

Video-Based 3D Human Pose Estimation Research

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Siting Tao Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China China

ISBN: (纸本)9781665409858

At present, most of the video-based 3D human pose estimation methods process the video frame by frame, ignoring the timing information of the upper and lower frames of the video. When there is a single-frame estimation error caused by self-occlusion, the occlusion part will be Some strange movements appear due to information loss, and the estimated results make it difficult to produce accurate motion sequences. Therefore, considering the continuity of the upper and lower frames of the video, this paper adopts the HMR algorithm and introduces a bidirectional long-term memory network (BI-LSTM) in the encoding part to process the video sequence, and then outputs the parameters of the human body model through the SMPL parameters and inputs them into the discriminator for confrontation, train. In order for the discriminator to also take into account the semantic information of the temporal context, the discriminator in this paper also introduces a BI-LSTM network to improve the authenticity of the generated poses. Experimental results show that the accuracy of the model is improved when self-occlusion occurs.

关键词： Training Industrial electronics Solid modeling Estimation error Three-dimensional displays Biological system modeling Pose estimation

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Masked Transformer-based Multi-GAN for 5G Core Network KPI Anomaly Detection

Masked Transformer-based Multi-GAN for 5G Core Network KPI A...

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International Conference on Computer Supported Cooperative Work in Design

作者： Enze Zhao Peng Li Zhang Cheng Wenmao Liu Lei Nie Haizhou Bao Qin Liu Kai Zhang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China NSFOCUS Technologies Group Co. Ltd. School of Cyber Science and Engineering Wuhan University China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The fifth generation (5G) network is a crucial foundation for the industrial Internet. key performance indicator (KPI) anomaly detection in the 5G core network (5GC) plays a pivotal role in 5G applications. Some researchers have introduced Generative Adversarial Networks (GAN)-based techniques to detect anomalies. However, these methods remain limited, such as pattern collapse. In this paper, we propose MTMG, a Masked Transformer-based Multi-GAN model, to achieve highly accurate and robust anomaly detection. We use Transformer to learn the associations between data better. Specifically, MTMG employs multiple generators and a discriminator to deflect the pattern collapse dilemma. In addition, we introduce the mask mechanism to learn the normal distribution of data better and prevent the model degradation caused by anomalies in the training set. We also adopt a root cause strategy to locate the anomalies. Experimental results demonstrate that our model outperforms the baselines significantly in terms of detection performance.

关键词： 5G mobile communication Computational modeling key performance indicator Transformer cores Gaussian distribution Transformers Generative adversarial networks

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Cancan Wu Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall.

关键词： Training Runtime Computational modeling Superresolution Memory management Feature extraction Diffusion models Convolutional neural networks Image reconstruction Standards

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Summary of Scene Text Detection and Recognition

Summary of Scene Text Detection and Recognition

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yao Qin Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In recent years, scene text recognition has received much attention, and has a wealth of application scenarios, such as: photo translation, image retrieval, scene understanding and so on. However, the text in the scene is also faced with many problems, such as: light changes, deformation text, text string recognition under background noise interference, text skew and degree of curvature, and a large number of artistic fonts. Solving the above problems will always be a challenging thing. This paper reviews some recent work on text detection, text recognition and end-to-end text detection and recognition, and finally looks forward to the future development direction.

关键词： Industrial electronics Image recognition Text recognition Interference Market research Noise measurement Strain

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BIRA-NET: BILINEAR ATTENTION NET FOR DIABETIC RETINOPATHY GRADING

arXiv

引用

arXiv 2019年

作者： Zhao, Ziyuan Zhang, Kerui Hao, Xuejie Tian, Jing Chua, Matthew Chin Heng Chen, Li Xu, Xin Institute of Systems Science National University of Singapore Singapore University of Electronic Science and Technology of China China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Eye protection

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A Method of Cattle Follicle Ultrasound Images Detection Based on HOG + Improved LBP + SVM

A Method of Cattle Follicle Ultrasound Images Detection Base...

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IEEE Conference on Industrial Electronics and Applications

作者： Yong Lv Jun Liu Wenhao Zeng Hubei Province Key Laboratory of Intelligent Information Processing and Real Time Industrial System Wuhan University of Science and Technology Wuhan

To improve the diagnostic accuracy of follicle ultrasound images detection, this paper proposed a method of cattle follicle ultrasound images detection based on HOG + Improved LBP + SVM. It calculated the Histogram of Oriented Gradient (HOG) feature for all cell in detection window, used improved Local Binary Pattern method to get gray feature, combined with the Support Vector Machine (SVM), it did the feature training and test experiment, last, the proposed method was compared with that single HOG feature detection, single traditional LBP feature detection and HOG + traditional LBP feature detection. Experimental results showed, the proposed method can effectively describe and detect cattle follicle ultrasound images, and it has higher recognition accuracy.

关键词： Histogram of Oriented Gradient (HOG) Local Binary Pattern (LBP) Support Vector Machine (SVM) Feature Combination

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Steel Surface Defect Detection Based on Improved YOLOv8

Steel Surface Defect Detection Based on Improved YOLOv8

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jichen Zhang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

There are some defects on the surface of steel that are difficult to detect, so we propose an improved algorithm based on YOLOv8 for detecting defects that are difficult to be detected. This improvement includes the introduction of an SPD module to minimize the problem of information loss due to downsampling; the addition of a small object detection head combined with EMA attention to further enhance the detection capabilities for small targets; the use of BiFPN to replace the original PANet, applying different weights to different features to strengthen feature fusion. Experiments were conducted on the NEU-DET dataset, and the algorithm improved mAP to 80.2%, an improvement of 3.9% over the baseline model.

关键词： Convolution Computational modeling Object detection Feature extraction Knowledge discovery Steel Data mining Background noise Defect detection Fuzzy systems

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