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Optical Convolutional Neural Network and Reservoir Computing with EO Polymer/Si Hybrid Optical Modulator

Optical Convolutional Neural Network and Reservoir Computing...

引用

OptoElectronics and communications conference, OECC

作者： Akito Shinya Koji Kida Hiromu Sato Guo-Wei Lu Shiyoshi Yokoyama Junichi Fujikata Graduate School of Science and Technology for Innovation Tokushima University Tokushima Japan Fuculty of Engineering and Design Kagawa University Takamatsu Japan Institute for Materials Chemistry and Engineering Kyushu University Kasuga Japan Institute of Post-LED Photonics Tokushima University Tokushima Japan

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

Convolutional neural networks are a powerful category of artificial neural networks. In this paper, we demonstrated a high-speed optical convolutional neural network accelerator at 100Gbaud, using EO polymer/Si hybrid optical modulator. In addition, optical reservoir computing with time-delayed optical circular configuration implemented with a free-carrier induced nonlinear waveguide was examined and efficient neuromorphic processing was demonstrated.

关键词： Optical filters Optical computing Optical fiber networks Optical imaging image restoration Optical modulation Nonlinear optics Electro-optical waveguides Optical polymers High-speed optical techniques

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Glove-Ing Attention: A Multi-Modal Neural Learning Approach to image Captioning

Glove-Ing Attention: A Multi-Modal Neural Learning Approach ...

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), ieee International conference on

作者： Lars Halvor Anundskås Hina Afridi Adane Nega Tarekegn Muhammad Mudassar Yamin Mohib Ullah Saira Yamin Faouzi Alaya Cheikh Department of Computer Science Norwegian University of Science and Technology (NTNU) Norway Geno SA Storhamargata 44 Hamar Norway Department of Management Sciences CUI Wah Campus Wah Cantt Pakistan

Articulating pictures using natural language is a complex undertaking within the realm of computer vision. The process of generating image captions involves producing depictions of images which can be achieved through advanced learning frameworks utilizing convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Yet, conventional RNNs face challenges such as gradient explosion and vanishing gradients, resulting in inferior outcomes when producing non-evocative sentences. In this paper, we proposed an encoder-decoder deep neural network to generate image captions using state-of-the-art backbone architecture EfficientNet as the encoder network. We used multimodal gated recurrent units (GrU) for the decoder, which incorporate GloVe word embeddings for the text data and visual attention for the image data. The network is trained on three different datasets, Indiana Chest X-ray, COCO and WIT, and the results are evaluated on the standard performance metrics of BLEU and METEOR. The quantitative results show that the network achieves promising results compared to the state-of-the-art models. The source code is publically available at https://***/larswise/imagecaptioning/src/master/wit_pipeline/.

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Relevant Tag Extraction Based on image visual Content 1st

Relevant Tag Extraction Based on Image Visual Content

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1st International conference on Applied Intelligence, ICAI 2023

作者： Fazal, Nancy Fränti, Pasi School of Computing University of Eastern Finland Joensuu Finland

ISBN: (纸本)9789819708260

Social media web services like Flickr allow users to share and freely annotate images with textual tags. These tags play a crucial part for text-based social image retrieval and browsing tasks. However, these tags are usually irrelevant and incomplete which limits their effectiveness and use. One fundamental problem is to interpret the relevance of tags with respect to the image visual content. Existing solutions have targeted either image visual content or user contributed tags separately to address the issue. Our proposed method Tag-tag, however, combines both aspects and exploit their semantic relationship. We use state of the art pretrained machine learning models in Tensorflow for object detection and NLP (Natural Language processing) for semantic analysis. Our experiments on a dataset of 219 randomly collected Flickr images demonstrates the applicability of our method. images with missing tags were excluded from the experiments. We identified two reasons where relevant tag was not found as: (1) relevant tag itself was missing from user contributed tag list, (2) relevant tag got skipped because it was either not found in the Wordnet dictionary or has no pretrained vector in word2vec-google-news-300 model. We identify this as one limitation of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Disentangled Prompt Learning for Transferable, Multimodal, Few-Shot image Classification

Disentangled Prompt Learning for Transferable, Multimodal, F...

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ieee International conference on Big Data

作者： John Yang Alessandro Magnani Binwei Yang Stanford University Stanford California United States Walmart Global Tech Sunnyvale California United States

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Existing prompting strategies for adapting pretrained vision language models to the downstream task of finegrained attribute classification learn visual variance in a class-specific manner. We present DisPoL, a method for learning disentangled representations that improves the transferability and performance of continuous prompts for downstream classification tasks. Our method decomposes a prompt into separate sub-prompts, then performs late fusion of the corresponding output embeddings in a novel manner. We combine the fixed embedding of a static, context-constraining object sub-prompt and the tunable embedding of a soft sub-prompt for a task-specific attribute using self-attention. By avoiding joint learning of these tokens, the resulting disentangled prompt embeddings are more transferable to unseen objects. We also demonstrate how to use hand-crafted templates to initialize the task-specific soft prompt, improving training efficiency. Through extensive experiments, we show that DisPoL exceeds the performance of existing methods in few-shot settings and highlight its contribution as a parameter-efficient fine-tuning method.

关键词： Training visualization Adaptation models image processing Computational modeling Predictive models Big Data Data models image classification

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Polygonal Point Set Tracking

Polygonal Point Set Tracking

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

作者： Nam, Gunhee Heo, Miran Oh, Seoung Wug Lee, Joon-Young Kim, Seon Joo Lunit Inc Seoul South Korea Yonsei Univ Seoul South Korea Adobe Res Redmond WA USA

ISBN: (纸本)9781665445092

In this paper, we propose a novel learning-based polygonal point set tracking method. Compared to existing video object segmentation (VOS) methods that propagate pixel-wise object mask information, we propagate a polygonal point set over frames. Specifically, the set is defined as a subset of points in the target contour, and our goal is to track corresponding points on the target contour. Those outputs enable us to apply various visual effects such as motion tracking, part deformation, and texture mapping. To this end, we propose a new method to track the corresponding points between frames by the global-local alignment with delicately designed losses and regularization terms. We also introduce a novel learning strategy using synthetic and VOS datasets that makes it possible to tackle the problem without developing the point correspondence dataset. Since the existing datasets are not suitable to validate our method, we build a new polygonal point set tracking dataset and demonstrate the superior performance of our method over the baselines and existing contour-based VOS methods. In addition, we present visual-effects applications of our method on part distortion and text mapping.

关键词： Learning systems Computer vision Target tracking image analysis Object segmentation visual effects Distortion

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Performance Evaluation of various Enhancement Algorithm on Thermal images by Implementation on PYNQ-Z2 Board

Performance Evaluation of various Enhancement Algorithm on T...

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ieee Region 10 Humanitarian Technology conference (R10-HTC)

作者： Kr Prabhakar Chaubey Anshu Saurabh Basant Kumar Himanshu Singh Electronics and Communication Engineering Department Motilal Nehru National Institute of Technology Allahabad Prayagraj India DRDO IRDE Dehradun India

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

This paper presents an efficient image enhancement technique based on detail guided image filtering algorithm implemented on the Python Productivity for ZYNQ (PYNQ-Z2) board, a flexible platform for developing real-time applications in diverse domains. The proposed work utilizes detail guided image filtering (DGIF) method to enhance details of thermal image while preserving essential structures in the thermal image. It provides a flexible environment using the PYNQ-Z2 for controlling the enhancement process by improving the visual appeal and identifying key elements in an image. Performance is evaluated by comparing the techniques with the other variants such as Global histogram equalization (GHE), Contrast limited adaptive histogram equalization (CLAHE), and Brightness Preserving Bi-Histogram Equalization (BBHE). For the quality assessment, computing the parameter such as PSNR, SSIM, and EDGE SCORE along with the computation time. It is observed that the guided image filtering algorithm for the thermal image enhancement and its hardware implementation on the PYNQ-Z2 board, offering faster processing compared to traditional software-based approaches, significantly reducing computational time. Application of this thermal image enhancement boosts surveillance systems by delivering more explicit images, better detection, and enhanced analysis, improving security and more efficient monitoring across various application.

关键词： Histograms visualization Surveillance Adaptive equalizers image filtering Real-time systems Thermal analysis Security Time factors image enhancement

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Performance Analysis of Adaptive filter Algorithms on Different Noise Sources 12

Performance Analysis of Adaptive filter Algorithms on Differ...

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12th International conference on Computer Communication and Informatics, ICCCI 2022

作者： Sugadev, M Kaushik, Malladi Vijayakumar, V. Ilayaraaja, K.T. Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Sathyabama Institute of Science and Technology Department of Electronics and Communications Chennai India

ISBN: (纸本)9781665480352

Noise has become a significant concern in every domain. For instance, in image processing, we can see background noise when we take a snap, also in the field of communication, the information is corrupted by the noises present in the environment, and at the time of decryption, it is becoming challenging. Back then, in earlier days, discrete filters that had fixed frequency response were used to minimize the level of Noise in the information signals. But these filters were not effective as most noise sources have a flat wideband spectrum. After the availability of digital signal processors, to obliterate the wideband Noise, adaptive filters are frequently used in communication systems and digital signal processing systems to filter noisy signals. The Adaptive Noise Cancellation (ANC) approach helps to eliminate the Noise by altering its transient parameters dependent on the incoming signal. In this article, the performance of LMS, NLMS and RLS algorithms is studied for various types of ambient noises. A speech signal that is corrupted by engine noise, waterfall noise, and audio noise and with echo are applied to an ANC filter and the improvement in signal to noise ratio is evaluated with different adaptive filter algorithms. © 2022 ieee.

关键词： Adaptive filters

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Parameterized Translational Motion Compensation for ISAR Imaging under low SNR

Parameterized Translational Motion Compensation for ISAR Ima...

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ieee International conference on Signal and image processing (ICSIP)

作者： Tingting He Biao Tian Yu Wang Shuai Li Jian Li Shiyou Xu School of Electronics and Communications Engineering Sun Yat-sen University Shenzhen China

In this paper, a new method for high-resolution ISAR imaging under low SNR using parameterized translational motion compensation is proposed. Firstly, translational motion of the target is modeled as formula of the polynomial coefficient vector. Then, entropy minimization corresponding to range profile with compensation term based on coefficients above is taken as objective function. Moreover, the Golden Section Search (GSS) is utilized to search the optimal parameters, which are estimated precisely and efficiently to implement motion compensation. Experimental results from both simulated and real data verify that the proposed method can achieve high-resolution fine focused ISAR image under low SNR with validity and robustness.

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Improving Open-vocabulary Video visual Relation Detection with Decomposed Prompt Learning and Relation Adjustment

Improving Open-vocabulary Video Visual Relation Detection wi...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ming Pei Yi Tan Yanbin Hao Hao Zhang Jinmeng Wu Basura Fernando Xun Yang School of Information Science and Technology University of Science and Technology of China Hefei China Centre for Frontier AI Research Agency for Science Technology and Research(A*STAR) Singapore Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Open-vocabulary video visual relation detection (VidVRD) expands the scope of detecting object relations in videos to include unseen categories. It marks considerable advancement in recognizing novel relations solely by training on a base set, thus extending the frontiers of automated video understanding. However, the performance of current methods on novel predicates remains significantly inferior to that on base categories. We attribute this discrepancy to two primary factors: (1) A significant task misalignment between the visual Relation Detection (VRD) task and the pre-trained models’ visual feature extractors, which are often designed for tasks like video-text retrieval and image-text retrieval, resulting in poor generalization to the novel set. (2) The relatively small size and limited vocabulary of open-vocabulary datasets, which create a substantial gap between base and novel predicates. Consequently, text prompts trained on the base set fail to generalize effectively to the novel set. To address these issues, we propose two improvement measures: (1) We decompose base and novel relations into actional and spatial patterns and introduce an innovative text prompt learning method that leverages the shared patterns between base and novel relations. (2) We develop a relation probability adjustment mechanism that utilizes reliable base relation predictions to adjust the probabilities of relations in novel classes by considering their overlaps in either actional or spatial contents. Experimental results on the benchmark dataset demonstrate significant performance improvements.

关键词： Training Learning systems visualization Vocabulary Signal processing Benchmark testing Feature extraction Acoustics Reliability Speech processing

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A Completed Gaussian Extended Binary Pattern for Texture image Classification 23

A Completed Gaussian Extended Binary Pattern for Texture Ima...

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Proceedings of the 2nd International conference on Signal processing, Computer Networks and communications

作者： Xiaochun Xu Weizheng Lin Dingrong Chen Nanling Su Leinuo Tang Changxu Cai College of Computer and Control Engineering Minjiang University China International Digital Economy College Minjiang University China

ISBN: (纸本)9798400716430

Texture image classification is a fundamental and challenging visual task and has wide range of applications. Binary pattern methods play an important role in texture feature extraction due to its ease of implementation and promising performance. To extract completed texture feature representation and improve the classification performance, this paper proposes a completed gaussian extend binary pattern for texture image classification. First, instead of the original pixel value, this paper uses the mean of local range to encode the binary pattern. Second, this paper introduces a novel gaussian sign pattern to fully represent the macro texture structure. Third, to achieve completed texture feature description, this paper presents a completed gaussian extended binary pattern, which combines the novel gaussian sign pattern, the local sign and magnitude pattern extracted from mean-processing texture image. To validate the effectiveness of the proposed completed gaussian extend binary pattern, experimental evaluations are conducted on three test subsets from Outex database. The evaluation results show that the proposed completed gaussian extended binary pattern achieves the state-pf-the-art classification performance.

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