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Semantically Grounded Visual Embeddings for Zero-Shot Learning

Semantically Grounded Visual Embeddings for Zero-Shot Learni...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Nawaz, Shah Cavazza, Jacopo Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Deutsch Elektronen Synchrotron DESY Hamburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Zero-shot learning methods rely on fixed visual and semantic embeddings, extracted from independent vision and language models, both pre-trained for other large-scale tasks. This is a weakness of current zero-shot learning frameworks as such disjoint embeddings fail to adequately associate visual and textual information to their shared semantic content. Therefore, we propose to learn semantically grounded and enriched visual information by computing a joint image and text model with a two-stream network on a proxy task. To improve this alignment between image and textual representations, provided by attributes, we leverage ancillary captions to provide grounded semantic information. Our method, dubbed joint embeddings for zero-shot learning is evaluated on several benchmark datasets, improving the performance of existing state-of-the-art methods in both standard (+1.6% on aPY, +2.6% on FLO) and generalized (+2.1% on AWA2, +2.2% on CUB) zero-shot recognition.

关键词： Learning systems Visualization Computational modeling soft sensors conferences Semantics Benchmark testing

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User recognition based on Gait pattern via Smartwatch Accelerometer in Unrestricted Environment 3

User Recognition based on Gait Pattern via Smartwatch Accele...

引用

3rd international conference on Communication, computing and Digital Systems, C-CODE 2023

作者： Khan, Sheharyar Shah, Syed M Adnan Arsalan, Aamir Ahmed, Wakeel University of Engineering and Technology Taxila Department of Computer Science Taxila Pakistan Fatima Jinnah Women University Rawalpindi Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350332391

Wearable gadgets are becoming a major constituent of our society due to a wide range of applications like financial transactions, unlocking automobiles, tracking health and fitness, and many more. Personal data is usually required to manage these services, however, most commercially available wearables devices either lack a user authentication mechanism or employ knowledge-based authentication like passwords, PINs, or pattern locks. In this growing digital age, it is really difficult for an individual to memorize such a huge number of passwords. In this research, we put forward a technique for recognizing users from their walking patterns recorded in real-life settings using a smartwatch accelerometer sensor. Ensemble classification techniques (bagging and boosting) are used for the purpose of user identification using the selected time domain features. Our proposed method attained the highest classification accuracy of 88.35% which transcends the existing user recognition schemes available in the literature. © 2023 IEEE.

关键词： Machine learning

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OAM pattern recognition Method Based on ResNet-18 Migration Learning 26

OAM Pattern Recognition Method Based on ResNet-18 Migration ...

引用

26th ACIS international Winter conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing, SNPD-Winter 2023

作者： Li, Zhong Yang, Xiaoxia Gao, Zhenning Cheng, Zehui North University of China School of Software Taiyuan China

ISBN: (纸本)9798350345865

Aiming at the application of Orbital Angular Momentum (OAM) in the field of optical communication, this paper proposes an OAM pattern recognition method based on ResNet-18 transfer learning. The OAM mode is a light wave with a spiral phase structure, which has the characteristics of high degree of freedom and large capacity, and has broad application prospects in the field of optical communication. However, detection and classification of OAM modes is difficult due to their complex wavefront structures. To address this issue, we use a pretrained ResNet-18 model to classify OAM patterns. This method uses the pre-trained ResNet-18 model for feature extraction and fine-tuning, which can effectively improve the classification performance of the model. Through experimental verification, the method has high accuracy and stability in the classification task of OAM mode. The research in this paper provides strong support for the application of OAM technology, and has certain reference value for image classification problems in other fields. © 2023 IEEE.

关键词： Angular momentum

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Multi-feature Co-learning for Image Inpainting 26

Multi-feature Co-learning for Image Inpainting

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Lin, Jiayu Wang, Yuan-Gen Tang, Wenzhi Li, Aifeng Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou Peoples R China Guangzhou Univ Network Ctr Guangzhou Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Image inpainting has achieved great advances by simultaneously leveraging image structure and texture features. However, due to lack of effective multi-feature fusion techniques, existing image inpainting methods still show limited improvement. In this paper, we design a deep multi-feature co-learning network for image inpainting, which includes soft-gating Dual Feature Fusion (SDFF) and Bilateral Propagation Feature Aggregation (BPFA) modules. To be specific, we first use two branches to learn structure features and texture features separately. Then the proposed SDFF module integrates structure features into texture features, and meanwhile uses texture features as an auxiliary in generating structure features. Such a co-learning strategy makes the structure and texture features more consistent. Next, the proposed BPFA module enhances the connection from local feature to overall consistency by co-learning contextual attention, channel-wise information and feature space, which can further refine the generated structures and textures. Finally, extensive experiments are performed on benchmark datasets, including CelebA, Places2, and Paris StreetView. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art. The source codes are available at https://***/GZHU-DVL/MFCL-Inpainting.

关键词： Source coding Benchmark testing Aerospace electronics pattern recognition

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Construction of Delaunay Triangles for Face recognition in Images 14

Construction of Delaunay Triangles for Face Recognition in I...

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Arunarani, A.R. Samuel, Prithi Sumathy, G. Maheshwari, A. Department of Computational Intelligence SRMIST Kattankulathur Campus Chennai India

A Delaunay graph method is a proposed solution to the matching issue in facial recognition. The techniques for feature matching and feature detection are scale-invariant feature transform (SIFT) and speeded-up robust features (SURF). In the existing method, the major drawback is that each algorithm only extracts particular features, leading to many mismatches in matched features. To avoid those mismatches, this paper concatenates the SIFTSURFLBPCNN together. Input images are pre-processed and key points are detected using sift algorithm. For each Detected key point, textural, and geometrical uutilizing, characteristics are extracted scale-invariant feature transform(SIFT), speeded-up robust features (SURF), local binary pattern(LBP), Convolution neural network(CNN) and concatenated to form a feature vector. Using the lows method, the key points between the input images are filtered out. Then the Delaunay triangles are constructed for the input images from their respective filtered key points using the Delaunay triangulation method. Testing is done and each algorithm’s performance is evaluated. © Grenze Scientific Society, 2023.

关键词： Local binary pattern

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2nd international conference on Data Analytics and Learning, DAL 2022

2nd International Conference on Data Analytics and Learning,...

引用

2nd international conference on Data Analytics and Learning, DAL 2022

ISBN: (纸本)9789819963454

The proceedings contain 20 papers. The special focus in this conference is on Data Analytics and Learning. The topics include: Nitrogen Deficiency and Yield Estimation in Paddy Field;medical Image Compression Using Huffman Coding for Tiff Images;Efficient Wavelet Based Denoising Technique Combined with Features of Cyclespinning and BM3D for Grayscale and Color Images;PBRAMEC: Prioritized Buffer Based Resource Allocation for Mobile Edge computing Devices;surface Water Quality Analysis Using IoT;children Facial Growth pattern Analysis Using Deep Convolutional Neural Networks;classification of Forged Logo Images;detection, Classification and Counting of Moving Vehicles from Videos;two-Stage Word Spotting Scheme for Historical Handwritten Devanagari Documents;face recognition Using Sketch Images;3D Object Detection in Point Cloud Using Key Point Detection Network;IRIS and Face-Based Multimodal Biometrics Systems;a Survey on the Detection of Diseases in Plants Using the Computer Vision-Based Model;an Approach to Conserve Wildlife Habitat by Predicting Forest Fire Using Machine Learning Technique;A Method to Detect Phishing Websites Using Distinctive URL Characteristics by Employing Machine Learning Technique;aquaculture Monitoring System: A Prescriptive Model;machine Learning-Based pattern recognition Models for Image recognition and Classification;a Review of Silk Farming Automation Using Artificial Intelligence, Machine Learning, and Cloud-Based Solutions.

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Intrusion Detection on Industrial Networks Using Neuromorphic computing 6

Intrusion Detection on Industrial Networks Using Neuromorphi...

引用

6th international conference on Electrical, Control and Instrumentation Engineering, ICECIE 2024

作者： Mehrabi, Ali Van Schaik, Andre International Centre for Neuromorphic Systems Western Sydney University Australia

ISBN: (纸本)9798350380040

Cyber-attacks on Industrial Control Systems (ICS) present critical risks to operational stability, public safety, and national security. As industrial networks become more integrated and interconnected, their susceptibility to sophisticated cyber threats increases. Traditional security measures often fall short in detecting evolving attack patterns. This paper investigates the application of neuromorphic computing, which emulates the human brain's pattern recognition capabilities, as an innovative approach to intrusion detection in industrial networks. We introduce a hardware-based Spiking Neural Network (SNN) engineered to monitor and analyze communication packets in real time, detecting anomalies indicative of potential intrusions. The neuromorphic approach offers a promising solution to the growing challenge of cyber-attacks in critical infrastructure. The proposed system is implemented on an FPGA, focusing initially on monitoring key packet types such as LLDP, ARP, UDP, TCP, and Profibus RT, with scalability to accommodate additional protocols. The results demonstrate the efficacy of this SNN-based intrusion detection system in providing real-time, efficient, and accurate monitoring of industrial networks. © 2024 IEEE.

关键词： Network intrusion

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A Comprehensive Portrayal of Research Drifts in Islanding Detection Methods towards soft computing 2

A Comprehensive Portrayal of Research Drifts in Islanding De...

引用

2nd international conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2022

作者： Bhushan, Indu Chandra, Subhash Yadav, Aravind Institute of Engineering Technology GLA Mathura India

ISBN: (纸本)9781665432153

This paper represents survey of various signal processing advancement and supported soft computing techniques for detecting islanding disturbances that occur in distributed generation (DG) based power system which is interconnected. Variety of latent islanding procedures, for example, Under/Over Voltage and Under/Over recurrence, Voltage Phase Jump Detection, Harmonic estimation, Voltage unbalance and so on are examined broadly for distinguishing proof of islanding situations. Further, progressed signal preparing, and delicate registering procedures are being examined for recognition or recognizing objectives and are being thought or ruminated about. It is featured that the sign handling and delicate processing strategies give quicker recognition with non-discovery zone (NDZ). These procedures are seen to be stronger and more adaptable in managing complex nonlinear frameworks. An examination review between the distinctive islanding methods is or has been introduced to know various merits and demerits of each. Advancing it with support to soft computing technique in islanding detection. © 2022 IEEE.

关键词： soft computing

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Review on detection and classification of underlying causes of power quality disturbances using signal processing and soft computing technique

Review on detection and classification of underlying causes ...

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international conference on Artificial Intelligence and Energy Systems (AIES)

作者： Bonde, G. N. Paraskar, S. R. Jadhao, S. S. Shri St Gajanan Maharaj Coll Engn Shegaon 444203 India

The new age power system is highly subjected to PQ disturbances that require proper attention and address. The research in this field is mainly categorized into different parts such as mathematical modeling, basic PQ principles, standards, impact and solutions, sources, and analysis. There are several underlying causes behind the occurrence of the PQ disturbance. Therefore, it is important to address the exact underlying cause for proper mitigation of the PQ disturbance. There are several methods available in the literature, which concentrated on to detection and classification of power quality events rather than the root cause of the PQ events. An effective method for root cause identification of PQ events is the need of the day. This article covers a broad review of signal processing and soft computing techniques used for the detection & recognition of the underlying cause of it. This will help the researcher, engineers, designers working in the field of detection, recognition, and monitoring of power quality. The comparative study of existing methods used in the literature is tabulated. The major concerns and obstacles in categorizing the recognition of power quality disturbances are thoroughly examined and discussed. The potential for new researchers in the field of power quality disturbance detection and recognition of underlying causes is further explored in this review. Copyright (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the international conference on Artificial Intelligence & Energy Systems.

关键词： Underlying Cause of Power Quality Disturbances Signal Processing Methods soft computing Techniques

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Prediction and Incentive of Number of Participants in Rural Cultural Square Activities by Integrating pattern recognition Algorithm 3

Prediction and Incentive of Number of Participants in Rural ...

引用

3rd international conference on Intelligent computing, Communication and Convergence, ICI3C 2023

作者： Ding, Hui Lianyungang Technical College School of Marxism Lianyungang China

ISBN: (纸本)9798350343922

The traditional method relies on historical data and simple statistical model to predict the number of participants in rural cultural square activities, which cannot fully take into account various complex factors and changes, and it is difficult to accurately predict and motivate the number of participants in rural cultural square activities. By integrating pattern recognition algorithms, a feasible method is provided to help organizers better plan and manage rural cultural square activities, in order to achieve the goals of community development and social interaction. This paper collected a large number of rural cultural square activity data and preprocessed the collected data. It combined the Long Short-Term Memory (LSTM) model and the Convolutional Neural Network (CNN) model to build the CNN-LSTM model. According to the number of participants predicted by CNN-LSTM model and the characteristics of activities, the incentive strategy is formulated. The experimental results showed that the accuracy of CNN-LSTM model in predicting the number of participants in rural cultural square activities was as high as 95.8%. The integration of CNN model and LSTM model can effectively improve the prediction of the number of participants in rural cultural square activities and improve the incentive effect. © 2023 IEEE.

关键词： Prediction models

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