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Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers 48

Lattice-Free Sequence Discriminative Training for Phoneme-Ba...

引用

48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Yang, Zijian Zhou, Wei Schluter, Ralf Ney, Hermann Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany AppTek GmbH Aachen52062 Germany

ISBN: (纸本)9781728163277

Recently, RNN-Transducers have achieved remarkable results on various automatic speech recognition tasks. However, lattice-free sequence discriminative training methods, which obtain superior performance in hybrid models, are rarely investigated in RNN-Transducers. In this work, we propose three lattice-free training objectives, namely lattice-free maximum mutual information, lattice-free segment-level minimum Bayes risk, and lattice-free minimum Bayes risk, which are used for the final posterior output of the phoneme-based neural transducer with a limited context dependency. Compared to criteria using N-best lists, lattice-free methods eliminate the decoding step for hypotheses generation during training, which leads to more efficient training. Experimental results show that lattice-free methods gain up to 6.5% relative improvement in word error rate compared to a sequence-level cross-entropy trained model. Compared to the N-best-list based minimum Bayes risk objectives, lattice-free methods gain 40% - 70% relative training time speedup with a small degradation in performance. © 2023 ieee.

关键词： neural transducer sequence discriminative training Speech recognition

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A Study of Convolutional Neural Networks in Face recognition

A Study of Convolutional Neural Networks in Face Recognition

引用

2021 International conference on computer Graphics, Artificial Intelligence, and Data processing. ICCAID 2021

作者： Song, Zhiben Yu, Yilun Department of Computer Science and Technology University of Sydney Sydney Australia Department of Automation University of Science and Technology Beijing Beijing China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Face recognition technology has attracted much attention in the past few years because of its potential value of both research and applications in the fields of pattern recognition, image analysis, computer vision and other fields. Meanwhile, as an important branch of the deep learning family, convolutional neural network (CNN) methods has yield remarkable achievements in many large-scale recognition tasks in the field of computer vision, especially face recognition. This is because, compared to classic neural networks, CNN has more hidden layers, a more complicated network topology, and more robust feature learning and feature expression capabilities. This paper gives an overview of the development on face recognition using CNN approaches, including categories of current face recognition technologies, description of the CNN method, and applications and research progress on CNN for face recognition. © 2022 SPIE.

关键词： Face recognition

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Drop the GAN: In Defense of Patches Nearest Neighbors as Single image Generative Models

Drop the GAN: In Defense of Patches Nearest Neighbors as Sin...

引用

2022 ieee/CVF conference on computer Vision and pattern recognition, CVPR 2022

作者： Granot, Niv Feinstein, Ben Shocher, Assaf Bagon, Shai Irani, Michal The Weizmann Institute of Science Dept. of Computer Science and Applied Math Israel Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks. Moreover, they offered the opportunity not only to manipulate a given image, but also to generate a large and diverse set of different outputs from a single natural image. This gave rise to new tasks, which are considered 'GAN-only'. However, despite their impressiveness, single-image GANs require long training time (usually hours) for each image and each task and often suffer from visual artifacts. In this paper we revisit the classical patch-based methods, and show that - unlike previously believed - classical methods can be adapted to tackle these novel 'GAN-only' tasks. Moreover, they do so better and faster than single-image GAN-based methods. More specifically, we show that: (i) by introducing slight modifications, classical patch-based methods are able to unconditionally generate diverse images based on a single natural image;(ii) the generated output visual quality exceeds that of single-image GANs by a large margin (confirmed both quantitatively and qualitatively);(iii) they are orders of magnitude faster (runtime reduced from hours to seconds). 22This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 788535), and the Carolito Stiftung. Dr Bagon is a Robin Chemers Neustein AI Fellow. © 2022 ieee.

关键词： Training Deep learning Visualization Technological innovation computer vision Runtime Europe

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Profiling Language Sign Using Deep Learning 10

Profiling Language Sign Using Deep Learning

引用

10th International conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Elbaghazaoui, Bahaa Eddine Amnai, Mohamed Fakhri, Youssef Choukri, Ali Gherabi, Noreddine IbnTofail University Laboratory of Computer Sciences Faculty of Sciences Kenitra Kenitra Morocco Sultan Moulay Slimane University National School of Applied Sciences Khouribga Morocco

ISBN: (纸本)9798350329674

Large segments of society place a high value on sign language as a distinct communication language. In computer vision, gesture detection is still a developing phenomenon and a hot topic. Aside from its many applications in immersive game technology and human-computer interaction, gesture recognition has many applications in deaf-mute sign language interpretation. In order to profile language signs, we created a model of image processing.that recognizes Arabic hand sign language based on artificial neural networks, starting with the data collection process and continuing with data profiling, then using the deep learning model to train and identify gestures, all the way to the presentation of the results. We offer a method to help with the conversion of sign language into voice and text-based formats. The outcomes we have achieved are crucial, important and significant for our next achievements. © 2023 ieee.

关键词： Neural networks

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Robust Face Detection and recognition using image processing.and OpenCV 6

Robust Face Detection and Recognition using Image Processing...

引用

6th International conference on Computing Methodologies and Communication, ICCMC 2022

作者： Bhat, Aruna Jha, Rahul Kumar Kedia, Vansh Delhi Technological University Department of Computer Engineering Delhi India

ISBN: (纸本)9781665410281

An enormous progress in the fields of Artificial Intelligence, Machine Learning, and computer Vision is witnessed over the last decade, owing to a doubling in processing.capacity per year, as well as a massive rise in data internationally. Facial identity, biometrics, and face identification have gone from a rarefied issue to a prominent topic for academics in computer science these days. Due to the basic nature of the topic, breakthroughs in computer image processing.and understanding studies will give insights into how the human brain operates, according to the common belief. Face detection and recognition are often employed in national defense, CCTV surveillance, criminal investigation, personal protection, law enforcement, biometrics, and a variety of other applications. For this project, OpenCV, Haar cascade, NumPy, and Matplotlib Python packages are utilized. Haar-Cascades will be utilized for face detection, and OpenCV provides three built-in face recognizers: Face recognition algorithms such as Eigenfaces, Fisher faces, and Local binary pattern histograms are used. The approach employed in this research is literature review, which entails reading and examining numerous books or literature relevant to mathematical ideas that underpin the development of algorithms to detect a human's face, which are subsequently followed by implementation in code, particularly Python code. © 2022 ieee.

关键词： Face recognition

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Enhancing Precision Medicine through Artificial Neural Networks for Phenotyping and Risk Prediction of Rare Genetic Disorders 2

Enhancing Precision Medicine through Artificial Neural Netwo...

引用

2nd International conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kaliappan, Seeniappan Balaji, V. Socrates, S. Yamsani, Nagendar Kcg College of Technology Department of Mechatronics Engineering Karapakkam Tamil Nadu Chennai600097 India Loyola Institute of Technology Department of Mechanical Engineering Tamil Nadu Chennai600123 India Velammal Institute of Technology Department of Mechanical Engineering Tamil Nadu Chennai601204 India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal506371 India

ISBN: (纸本)9798350370188

In this study, we present an innovative approach to enhance precision medicine for rare genetic disorders by leveraging a comprehensive Artificial Neural Network (ANN) architecture. This architecture amalgamates three pivotal algorithms: Convolutional Neural Network (CNN) for image analysis, Recurrent Neural Network (RNN) for sequential clinical data, and Dense Neural Network (DNN) for feature integration and risk prediction. CNNs are specialized for efficient grid-like data handling, making them ideal for image analysis and pattern recognition. RNNs, on the other hand, excel in processing.sequential data due to their inherent ability to retain information from previous inputs. DNNs play a fundamental role in capturing intricate patterns and relationships in the data. Our approach strategically integrates these algorithms into a cohesive ANN framework, enabling a comprehensive analysis of genetic data, which is critical in the domain of rare genetic disorders. By leveraging the capabilities of CNN, RNN, and DNN, a comprehensive strategy emerges, enabling accurate risk prediction and phenotype analysis. This approach significantly contributes to the progression of precision medicine, specifically tailored for rare genetic disorders. © 2024 ieee.

关键词： Convolutional neural networks

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Subjective Quality Optimized Efficient image Compression

Subjective Quality Optimized Efficient Image Compression

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Wang, Xining Chen, Tong Ma, Zhan Nanjing Univ Vis Lab Nanjing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose an efficient image compression framework that is optimized for subjective quality. Our framework is mainly based on the NLAIC (NonLocal Attention Optimized image Coding) model which applied Variational Autoencoder (VAE) and non-local attention module to end-to-end image compression. This work makes two major contributions to the NLAIC framework. First, our models are optimized for subjective-friendly loss functions rather than conventional MSE (Mean Squared Error) or MS-SSIM (Multiscale Structural Similarity) which was widely used in previous works. Second, we introduce block-based inference mechanism to reduce the running memory consumption of the image compression network, and suggest a partial post-processing.step to alleviate block artifacts caused by block-based inference in a lightweight computational fashion. Experiments have proved that the image reconstructed by our method can preserve more texture details than models trained for optimal MSE or MS-SSIM and also present capability for high-throughput decoding.

关键词： Measurement computer vision image coding Inference mechanisms conferences Memory management Distortion

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Dilated Convolutional Transformer for High-Quality image Deraining

Dilated Convolutional Transformer for High-Quality Image Der...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Yufeng Li Jiyang Lu Hongming Chen Xianhao Wu Xiang Chen College of Electronic and Information Engineering Shenyang Aerospace University School of Computer Science and Engineering Nanjing University of Science and Technology

Convolutional neural networks (CNNs) and Transformers have achieved significant success in image signal processing. However, little effort has been made to effectively combine the properties of these two architectures to satisfy image deraining. In this paper, we propose an effective de-raining method, dilated convolutional Transformer (DCT), which can enlarge the receptive fields of the network to aggregate global information. The fundamental building unit of our approach is the dilformer block containing multi-dilconv sparse attention (MDSA) and multi-dilconv feed-forward network (MDFN). The MDSA calculates the multi-scale query to generate accurate similarity map so that rich multi-scale information can be better utilized for the high-quality image reconstruction. In addition, we adopt ReLU to replace the original softmax to enforce sparsity in the Transformer for better feature aggregation. The MDFN is further established to better integrate the rain information of different scales in the feature transformation. Extensive experiments on the benchmarks show the favorable performance against state-of-the-art approaches.

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A Model of Face Mask Wearing Normality Detection Based on Deep Learning

A Model of Face Mask Wearing Normality Detection Based on De...

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2022 International conference on image, Signal processing. and pattern recognition, ISPP 2022

作者： Dai, Jin Lu, Yuemeng Zhou, Xingxing College of of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510654846

Regulating the wearing of masks is an important means to effectively prevent respiratory infectious diseases, and safety risks can be reduced by using machines instead of humans for mask wear testing. In this paper, we propose a deep learning-based mask wearing normality detector. This method uses featu-re pyramids to fuse multi-level features and employs a multiscale detection strategy to improve the detection accuracy of face land-marks and masks. In addition, a context-sensitive predict module for facial landmarks and masks detection is also proposed. We compared the proposed model with Retina Net, YOLO4 and Faster R-CNN. The results show that the proposed model is superior to existing model for the normative detection of mask wearing. © 2022 SPIE.

关键词： Deep learning

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Trimap generation with background for natural image matting 3

Trimap generation with background for natural image matting

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3rd International conference on Optics and Machine Vision, ICOMV 2024

作者： Fu, Qian Liang, Yihui Kun, Zou Feng, Fujian Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510680319

image matting is a widely-used image processing.technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TP-BG leverages a ViT backbone for feature extraction and employs the image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics. © 2024 SPIE.

关键词： Costs

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