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Semantic-visual Guided Transformer for Few-shot Class-incremental Learning

Semantic-visual Guided Transformer for Few-shot Class-increm...

引用

ieee International conference on Multimedia and Expo (ICME)

作者： Wenhao Qiu Sichao Fu Jingyi Zhang Chengxiang Lei Qinmu Peng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

Few-shot class-incremental learning (FSCIL) has recently attracted extensive attention in various areas. Existing FSCIL methods highly depend on the robustness of the feature backbone pre-trained on base classes. In recent years, different Transformer variants have obtained significant processes in the feature representation learning of massive fields. Nevertheless, the progress of the Transformer in FSCIL scenarios has not achieved the potential promised in other fields so far. In this paper, we develop a semantic-visual guided Transformer (SV-T) to enhance the feature extracting capacity of the pre-trained feature backbone on incremental classes. Specifically, we first utilize the visual (image) labels provided by the base classes to supervise the optimization of the Transformer. And then, a text encoder is introduced to automatically generate the corresponding semantic (text) labels for each image from the base classes. Finally, the constructed semantic labels are further applied to the Transformer for guiding its hyperparameters updating. Our SV-T can take full advantage of more supervision information from base classes and further enhance the training robustness of the feature backbone. More importantly, our SV-T is an independent method, which can directly apply to the existing FSCIL architectures for acquiring embeddings of various incremental classes. Extensive experiments on three benchmarks, two FSCIL architectures, and two Transformer variants show that our proposed SV-T obtains a significant improvement in comparison to the existing state-of-the-art FSCIL methods.

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Super Resolution MRI via Upscaling Diffusion Bridges

Super Resolution MRI via Upscaling Diffusion Bridges

引用

ieee Signal processing and communications Applications (SIU)

作者： Muhammad Usama Mirza Fuat Arslan Tolga Çukur Electrical and Electronics Engineering Bilkent University Ankara Turkey National Magnetic Resonance Research Center Bilkent University Ankara Turkey

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality that provides high-resolution anatomical information about tissues. However, the intrinsic trade-off between acquisition time and image quality poses challenges in obtaining high-resolution images within a clinically feasible timeframe. This study introduces a novel approach to acquire high-resolution images in short scan times based on Super-Resolution Diffusion Bridges (SRDB). The proposed method leverages advanced machine learning techniques based on diffusion models to upscale MR images. The While standard diffusion models learn a mapping from Gausssian distributed noise images to target images, SRDB instead learns a mapping from low-resolution MR images to high-resolution images. Unlike the task-independent learning in standard diffusion model, SRDB thus performs task-based learning to improve structural consistency and better preservation of anatomical features. In this way, the trained models help capture fine details that may be missed in standard low-resolution MRI acquisitions.

关键词： Bridges image quality Magnetic resonance imaging Superresolution Noise Diffusion models Task analysis

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RaScaNet: Learning Tiny Models by Raster-Scanning images

RaScaNet: Learning Tiny Models by Raster-Scanning Images

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yoo, Jaehyoung Lee, Dongwook Son, Changyong Jung, Sangil Yoo, ByungIn Choi, Changkyu Han, Jae-Joon Han, Bohyung Samsung Adv Inst Technol SAIT Suwon South Korea Seoul Natl Univ Comp Vis Lab Seoul South Korea

ISBN: (纸本)9781665445092

Deploying deep convolutional neural networks on ultralow power systems is challenging due to the extremely limited resources. Especially, the memory becomes a bottleneck as the systems put a hard limit on the size of on-chip memory. Because peak memory explosion in the lower layers is critical even in tiny models, the size of an input image should be reduced with sacrifice in accuracy. To overcome this drawback, we propose a novel Raster-Scanning Network, named RaScaNet, inspired by raster-scanning in image sensors. RaScaNet reads only a few rows of pixels at a time using a convolutional neural network and then sequentially learns the representation of the whole image using a recurrent neural network. The proposed method operates on an ultra-low power system without input size reduction;it requires 15.9-24.3x smaller peak memory and 5.3-12.9x smaller weight memory than the state-of-the-art tiny models. Moreover, RaScaNet fully exploits on-chip SRAM and cache memory of the system as the sum of the peak memory and the weight memory does not exceed 60 KB, improving the power efficiency of the system. In our experiments, we demonstrate the binary classification performance of RaScaNet on visual Wake Words and Pascal VOC datasets.

关键词： image sensors Deep learning visualization Memory management Signal processing algorithms Feature extraction System-on-chip

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Real-Time Drowsiness System Using image processing

Real-Time Drowsiness System Using Image Processing

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2022 International conference on Cyber Resilience, ICCR 2022

作者： Jindal, Aviral Deepika Joshi, Abhishek Chandigarh University Department of Computer Science Mohali India Uttaranchal University Dehradun248007 India

ISBN: (纸本)9781665461221

Over the ultimate decades, the site visitors' incidents find out about is turn out to be necessary due to the fact they promote various cases and damage round the globe. The assist in decreasing this casualty, in this document, a new Advanced Driver Assistances System for computerized drivers alert for drowsiness detection directly stores the visual data. According to Artificial Intelligent data is processed and action is taken. The device is completely made according to work automatic in every situation. This process is so fast that it collects data though visuals of eyes and face and then process is according to method used to take any prior action. Examples of one of a kind driver's pictures taken over authentic auto mobile are verified to authorize the procedure that mechanism in genuine time. For the final layout of the system is construct structures which can help us to enable us checking gestures of driver's face and eyes, and in mean time, to record their stage for interest throughout total riding process and protect from accident situation. © 2022 ieee.

关键词： Real time systems

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Super Resolution with Deep Learning in Thermal images

Super Resolution with Deep Learning in Thermal Images

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ieee Signal processing and communications Applications (SIU)

作者： Mehmet Can Öz Tuğba Selcen Navruz Gazi Üniversitesi FBE Elektrik Elektronik Mühendisliği Ana Bilim Dalı Ankara Türkiye ASELSAN A.Ş. Ankara Türkiye Gazi ÜniversitesiMühendislik Fakültesi Elektrik Elektronik Mühendisliği Bölümü Ankara Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Although traditional methods and deep learning-based methods have been used for color images in super-resolution applications for a long time, these methods have only recently begun to be used for thermal images. The fact that thermal sensors are generally low resolution and high resolution thermal sensors are very expensive has led to the need to increase the resolution in thermal images by using deep learning. In this study, the effect of bicubic interpolation, one of the traditional approaches, and SRGAN and ESRGAN architectures, one of the deep learning-based methods, on super resolution on thermal images were examined.

关键词： Learning systems image sensors Deep learning Interpolation Superresolution Color Thermal sensors

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Deep Convolutional Neural Network Based Antenna Scan Type Classification using Power-Time images

Deep Convolutional Neural Network Based Antenna Scan Type Cl...

引用

ieee Signal processing and communications Applications (SIU)

作者： Mustafa Talha Bayram Halim Sinan Balaban Behçet Uğur Töreyin Radar Elektronik Harp ve İstihbarat Sistemleri ASELSAN A.SS. Ankara Elektrik ve Elektronik Fakuiltesi İstanbul Teknik Üniversitesi Bilgisayar ve Bilisisim Fakiultesi İstanbul Teknik Üniversitesi

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In Electronic Warfare (EW) systems, one of the most critical tasks is the accurate identification of radars in the environment. During the identification process, various uncertainties may arise. Antenna Scan Type (AST) serves as a significant parameter for resolving these ambiguities. In this study, a method based on Deep Convolutional Neural Networks (DCNN) with input as power-time images is proposed for the AST classification problem. Unlike previous studies in the literature, this method can operate independently of the number of pulses. The classification process was conducted using a DCNN-based model, and the results were shared with the readers Upon examination of the test results, it is observed that the proposed method yields successful outcomes for the AST classification problem.

关键词： Electronic warfare Uncertainty image resolution Neural networks Radar Signal processing Radar imaging

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On the Interplay Between Network Metrics and Performance of Mobile Edge Offloading

On the Interplay Between Network Metrics and Performance of ...

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ieee International conference on communications (ICC)

作者： Parisa Fard Moshiri Murat Simsek Burak Kantarci School of Electrical Engineering and Computer Science at the University of Ottawa Ottawa ON Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Multi-Access Edge Computing (MEC) emerged as a viable computing allocation method that facilitates offloading tasks to edge servers for efficient processing. The integration of MEC with 5G, referred to as 5G-MEC, provides real-time processing and data-driven decision-making in close proximity to the user. The 5G- MEC has gained significant recognition in task offloading as an essential tool for applications that require low delay. Nevertheless, few studies consider the dropped task ratio metric. Disregarding this metric might possibly undermine system efficiency. In this paper, the dropped task ratio and delay has been minimized in a realistic 5G- MEC task offloading scenario implemented in NS3. We utilize Mixed Integer Linear Programming (MILP) and Genetic Algorithm (GA) to optimize delay and dropped task ratio. We examined the effect of the number of tasks and users on the dropped task ratio and delay. Compared to two traditional offloading schemes, First Come First Serve (FCFS) and Shortest Task First (STF), our proposed method effectively works in 5G-MEC task offloading scenario. For MILP, the dropped task ratio and delay has been minimized by 20% and 2ms compared to GA.

关键词： Measurement Schedules image edge detection Streaming media Real-time systems Delays Servers

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RACENet: Real-time Adaptive Class-aware Early-exit Networks for Edge Devices

RACENet: Real-time Adaptive Class-aware Early-exit Networks ...

引用

ieee International conference on Pervasive Computing and communications (PerCom)

作者： Mohammed Ayyat Mohamed Osman Tamer Nadeem College Of Engineering Virginia Commonwealth University Richmond VA USA

ISBN: (数字)9798331535513

ISBN: (纸本)9798331535520

As the integration of edge and IoT devices continues to surge, the need for streamlined machine learning solutions, notably Deep Neural Networks (DNNs), becomes paramount. However, the inherent computational demands and significant memory requirements of DNNs present hurdles for seamless edge integration. Early-Exit Networks (EENs) have emerged as a solution by adding early exits to DNNs, allowing for early inference and facilitating dynamic deployment on devices with varying capabilities. Despite their advantages, current EEN models uniformly handle samples from all classes, which is suboptimal in many edge scenarios where different classes require varying processing speeds and response times. Our research introduces RACENet, a novel architecture with adaptive early-exit capabilities that adjusts class prioritization in real-time. This flexibility enables RACENet to respond to evolving class priorities during runtime. Extensive evaluations on vision tasks, and network intrusion detection demonstrate that RACENet maintains accuracy while dynamically managing class priorities and accelerating inference for high-priority classes, all without adding additional computational burdens on constrained edge devices.

关键词： Accuracy Runtime image edge detection Network intrusion detection Artificial neural networks Aerodynamics Real-time systems Time factors Surges Testing

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Enhancing Daily Reports: Application of Multimodal GPT for image Description Generation in Construction Site Quality Assurance

Enhancing Daily Reports: Application of Multimodal GPT for I...

引用

image processing Challenges (ICIPC), ieee International conference on

作者： Chuan-Yu Chang Abida Khanum Hui-Huang Hsu Ming-Hsiu Tsai Wei-Ting Chen Sheng-Gui Su Intelligent Recognition Industry Service Research Center National Yunlin University of Science and Technology Department of Computer Science Information Engineering Tamkang University Department of Civil Engineering Tamkang University

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

In recent years, cutting-edge language models like GPT, TAIDE, LLaMa, and VLM have exhibited remarkable prowess across diverse natural language tasks. This surge in artificial intelligence and computer vision technologies has revolutionized image analysis, offering a fresh and viable approach. Yet, the management and analysis of construction site images persist as formidable challenges in construction projects. While these images harbor valuable insights, conventional manual analysis methods are plagued by inefficiency and error susceptibility. Our study introduces an innovative multimodal approach named Daily Report for Construction Site GPT (DrCS-GPT), designed to navigate the intricacies of building construction sites. The study was initiated by compiling a diverse dataset encompassing visual images and reports from building construction sites in collaboration with CECI Engineering Consultants, Inc., Taiwan. In the data acquisition, the focus shifted towards refining our proposed model through pre-training and fine-tuning processes. This model exhibits proficiency in generating high-quality textual content while addressing common issues such as fragmented sentences and word repetition. The methodology entailed dataset assembly, model refinement, and integration with advanced linguistic tools to ensure precision and coherence in scientific communication. Our results underscore the model’s exceptional performance on 1600 images, with inference times of just 0.21s per task.

关键词： Computer vision visualization image analysis Quality assurance Computational modeling Buildings Refining Security Artificial intelligence Surges

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Enhanced Diabetic Retinopathyanalysis: Unet-Based Lesion Segmentation Coupled with Mobilenetv2 for Feature Extraction and Efficientnetb0 for Classification

Enhanced Diabetic Retinopathyanalysis: Unet-Based Lesion Seg...

引用

ieee International conference on Intelligent Techniques in Control, Optimization and Signal processing (INCOS)

作者： Sankara Narayanan S Sayan Kumar Bag Shivansh Singh Dept of Computing Technologies SRM Institute of Science & Technology Kattankulatur India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

In this research, we address the critical issue of precise Diabetic Retinopathy (DR) diagnosis, a condition of ten leading severe vision impairment or blindness in diabetic patients. Leveraging advanced deep learning models and innovative image processing techniques, our study focuses on accurate retinal image segmentation using a UNet model. This segmentation method delineates lesions and retinal structures effectively. Subsequently, Gabor filters are applied for intricate texture pattern extraction, indicative of diverse retinopathy stages. Integrating MobileNetV2 for feature extraction and EfficientNetB0 for multi-class classification significantly enhances the diagnostic accuracy. Our developed system exhibits a promising 91.2% test accuracy, showcasing its potential in DR diagnosis. While challenges related to varying severity levels persist, our robust framework lays the groundwork for future refinements. By amalgamating sophisticated image segmentation, feature extraction, and classification techniques, our system provides a solid foundation for accurate and timely DR assessment. With continuous enhancements, including the incorporation of more extensive and diverse datasets, our approach holds the promise to revolutionize DR diagnostics. The integration of cutting-edge technology into medical practices underscores the transformative impact of artificial intelligence in the realm of ophthalmology, promising improved patient outcomes.

关键词： image segmentation Diabetic retinopathy visual impairment Feature extraction Retina Solids Gabor filters

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