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Text Enhancement Network for Complex Multi-line Scene Text image Super-resolution

Text Enhancement Network for Complex Multi-line Scene Text I...

引用

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

作者： Yang Liu Yuliang Huang Yiming Liu School of Computer Science and Engineering Sun Yat-sen University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Scene text image super-resolution aims to enhance low-resolution, spatially distorted text images by reconstructing high-resolution counterparts with clear textual structure and superior visual fidelity. Nevertheless, the prevalence of multi-line text in real-world scenarios presents a significant challenge to existing text image super-resolution methods. Current approaches predominantly focus on enhancing single-line text images, and their model architectures are inherently constrained when confronted with the complexity of multi-line text. This architectural limitation severely impedes their direct application to more realistic, multi-line text scenarios commonly encountered in natural images. To address these limitations, we propose a Text Enhancement Network for Complex Multi-line Scene Text image Super-resolution (ML-TSR), a novel framework specifically engineered for multi-line text image super-resolution. ML-TSR employs a two-stage approach: initially reconstructing high-resolution text images, followed by a refinement phase leveraging text-related loss derived from text detection and optimization modules. This process yields enhanced text recovery outcomes. Extensive evaluations on the Real-CE benchmark dataset demonstrate the superior performance of ML-TSR in text restoration quality compared to the state-of-the-art baseline methods.

关键词： visualization Text recognition Superresolution Text detection Speech recognition Speech processing Spatial resolution Optimization image reconstruction Signal resolution

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One-Shot Learning for Pose-Guided Person image Synthesis in the Wild

One-Shot Learning for Pose-Guided Person Image Synthesis in ...

引用

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

作者： Dongqi Fan Tao Chen Mingjie Wang Rui Ma Qiang Tang Zili Yi Qian Wang Liang Chang University of Electronic Science and Technology of China China Mobile Zijin Innovation Institute Zhejiang Sci-Tech University Jilin University University of British Columbia NanJing University China Mobile Research Institute

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current Pose-Guided Person image Synthesis (PGPIS) methods depend heavily on large amounts of labeled triplet data to train the generator in a supervised manner. However, they often falter when applied to in-the-wild samples, primarily due to the distribution gap between the training datasets and real-world test samples. While some researchers aim to enhance model generalizability through sophisticated training procedures, advanced architectures, or by creating more diverse datasets, we adopt the test-time fine-tuning paradigm to customize a pre-trained Text2image (T2I) model. However, naively applying test-time tuning results in inconsistencies in facial identities and appearance attributes. To address this, we introduce a visual Consistency Module (VCM), which enhances appearance consistency by combining the face, text, and image embedding. Our approach, named OnePoseTrans, requires only a single source image to generate high-quality pose transfer results, offering greater stability than state-of-the-art data-driven methods. For each test case, OnePoseTrans customizes a model in around 48 seconds with an NVIDIA V100 GPU.

关键词： Training visualization image synthesis Graphics processing units One shot learning Signal processing Stability analysis Generators Speech processing Tuning

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Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size

Boosting the Transferability of Adversarial Examples via Loc...

引用

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

作者： Junlin Liu Chenyu Zhang Xinchen Lyu National Engineering Research Center for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Adversarial examples are one critical security threat to various visual applications, where injected human-imperceptible perturbations confuse the output. Generating transferable adversarial examples in the black-box setting is crucial but challenging in practice. Existing input-diversity-based methods adopt different image transformations, but may be inefficient due to insufficient input diversity and an identical perturbation step size. Motivated by the fact that different image regions have distinctive weights in classification, this paper proposes a black-box adversarial generative framework by jointly designing enhanced input diversity and adaptive step sizes. We design local mixup to randomly mix a group of transformed adversarial images, strengthening the input diversity. For precise adversarial generation, we project the perturbation to relax the boundary constraint. Moreover, the step sizes of different regions can be dynamically adjusted by integrating a second-order momentum. Extensive experiments on imageNet validate that our framework can achieve superior transferability compared to state-of-the-art baselines. Code is available at https://***/ZeroWalker10/IDAA.

关键词： image transformation visualization Perturbation methods Diversity reception Closed box Transforms Signal processing Security Speech processing Optimization

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2021 International conference on visual communications and image processing, VCIP 2021 - Proceedings

2021 International Conference on Visual Communications and I...

引用

2021 International conference on visual communications and image processing, VCIP 2021

ISBN: (纸本)9781728185514

The proceedings contain 134 papers. The topics discussed include: large-scale crowdsourcing subjective quality evaluation of learning-based image coding;alpha-trimmed mean filter and XOR based image enhancement for embedding data in image;faster and finer pose estimation for object pool in a single RGB image;MPEG immersive video tools for light field head mounted displays;urban planter: a web app for automatic classification of urban plants;SPCNet: a panoramic image depth estimation method based on spherical convolution;attention-guided convolutional neural network for lightweight JPEG compression artifacts removal;and enhanced cross component sample adaptive offset for AVS3.

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Object-level Data Augmentation for visual 3D Object Detection in Autonomous Driving

Object-level Data Augmentation for Visual 3D Object Detectio...

引用

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

作者： Haoran Cheng Junkai Xu Liang Peng Zheng Yang Xiaofei He Boxi Wu Zhejiang University Hangzhou China FABU Inc Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Data augmentation plays an important role in visual-based 3D object detection. Existing detectors typically employ image/BEV-level data augmentation techniques, failing to utilize flexible object-level augmentations because of 2D-3D inconsistencies. This limitation hinders us from increasing the diversity of training data. To alleviate this issue, we propose an object-level data augmentation approach that incorporates scene reconstruction and neural scene rendering. Specifically, we reconstruct the scene and objects by extracting image features from sequences and aligning them with associated LiDAR point clouds. This approach is intended to conduct the editing process within a 3D space, allowing for flexible object manipulation. Additionally, we introduce a neural scene renderer to project the edited 3D scene onto a specified camera plane and render it onto a 2D image. Combined with scene reconstruction, it overcomes the challenges stemming from 2D/3D inconsistencies, enabling the generation of object-level augmented images with corresponding labels for model training. To validate the proposed method, we apply our method to various multi-camera 3D object detectors, consistently boosting the performance.

关键词： visualization Three-dimensional displays Laser radar Training data Detectors Object detection Data augmentation Rendering (computer graphics) Feature extraction image reconstruction

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DeepMatch: Navigating the Complexities of Underwater Textures for Enhanced Keypoint Matching

DeepMatch: Navigating the Complexities of Underwater Texture...

引用

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

作者： Sainan Zhang Tong Liu Zhe Wang Zhibin Yu Bing Zheng Faculty of Information Science and Engineering Ocean University of China Qingdao China Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Sanya China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Driven by demands for oceanic exploration, advancements in 3D visual tasks based on video frames are essential. Keypoint matching, essential for camera pose and motion, is hindered by the unique challenges of underwater imagery, such as sparse and repetitive textures. To tackle these issues, we introduce the DeepMatch framework, tailored for aquatic environments. It comprises two main components: a feature extraction network with a multi-level feature fusion module for enhanced keypoint detection in sparse textures, and an attention-based graph neural network with relative position encoding to correct matches in repetitive texture areas. Our experiments demonstrate that DeepMatch surpasses traditional and learning-based methods in underwater image matching, providing a robust solution for accurate camera pose estimation.

关键词： Learning systems visualization Three-dimensional displays Navigation image matching Pose estimation Speech enhancement Signal processing Feature extraction Cameras

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Multi-Prototype Grouping for Continual Learning in visual Question Answering

Multi-Prototype Grouping for Continual Learning in Visual Qu...

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

作者： Licheng Zhang Zhendong Mao Yixing Peng Zheren Fu Yongdong Zhang School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

visual Question Answering (VQA) aims to answer questions utilizing information from both textual and visual modalities. New data categories and novel combinations of the two modalities will continuously emerge in practical applications, necessitating continual learning. For this unique compositional generalization challenge, existing work adopts prototype learning to separately capture sample-invariant features of question types and image objects, retrieving the most similar prototype from two modalities as generalizable representations. However, for semantic fuzzy samples that deviate from the corresponding prototype, relying on a single prototype may lead to error-prone representation learning. Additionally, for the catastrophic forgetting challenge, existing methods employ memory to store past samples but lack an effective selection of samples to be stored, merely updating the memory through random sampling. In this paper, we propose ProtoGroup, a multi-prototype grouping continual learning framework for VQA, which groups prototypes based on their similarity to obtain more accurate and stable sample-invariant features. We also devise a memory sample selection method based on the correlation with prototype groups from old tasks. Experimental results demonstrate our framework outperforms previous methods significantly across multiple datasets and settings.

关键词： Continuing education Representation learning visualization Accuracy Semantics Prototypes Signal processing Data aggregation Question answering (information retrieval) Speech processing

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An Attentive Dual-Encoder Framework Leveraging Multimodal visual and Semantic Information for Automatic OSAHS Diagnosis

An Attentive Dual-Encoder Framework Leveraging Multimodal Vi...

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

作者： Yingchen Wei Xihe Qiu Xiaoyu Tan Jingjing Huang Wei Chu Yinghui Xu Yuan Qi School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China INFLY TECH (Shanghai) Co. Ltd. ENT Institute and Department of Otorhinolaryngology Eye & ENT Hospital Fudan University Shanghai China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Obstructive sleep apnea-hypopnea syndrome (OS-AHS) is a common sleep disorder caused by upper airway blockage, leading to oxygen deprivation and disrupted sleep. Traditional diagnosis using polysomnography (PSG) is expensive, time-consuming, and uncomfortable. Existing deep learning methods using facial image analysis lack accuracy due to poor facial feature capture and limited sample sizes. To address this, we propose a multimodal dual encoder model that integrates visual and language inputs for automated OSAHS diagnosis. The model balances data using randomOverSampler (ROS), extracts key facial features with attention grids, and converts basic physiological data into meaningful text. Cross attention combines image and text data for better feature extraction, and ordered regression loss ensures stable learning. Our approach improves diagnostic efficiency and accuracy, achieving 91.3% top-1 accuracy in a four class severity classification task, demonstrating state of the art performance. Code is available at https://***/luboyan6/VTA-OSAHS.

关键词： visualization Accuracy Semantics Signal processing Feature extraction Sleep apnea Physiology Data models Speech processing Facial features

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MS-Glance: Bio-Inspired Non-Semantic Context Vectors and Their Applications in Supervising image Reconstruction

MS-Glance: Bio-Inspired Non-Semantic Context Vectors and The...

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Ziqi Gao Wendi Yang Yujia Li Lei Xing S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China Department of Radiation Oncology Stanford University Stanford CA USA Key Laboratory of Precision and Intelligent Chemistry USTC CAS Hefei Anhui

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction, non-semantic information, such as global spatial structures, is often overlooked. To bridge the gap, we propose a biologically informed non-semantic context descriptor, MS-Glance, along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context, while a local Glance vector is a flattened local image window, mimicking a zoom in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://***/Z7Gao/MSGlance.

关键词： Computer vision visualization Magnetic resonance imaging Fitting Vectors image restoration Indexes image reconstruction Biomedical imaging visual perception

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A Comparative Analysis of Multi-Modal Medical image Fusion Techniques using MSVD, WPD, PCA, and DWT

A Comparative Analysis of Multi-Modal Medical Image Fusion T...

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Cognitive Computing in Engineering, communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

image fusion is a technique used in image processing to create a more comprehensive representation by combining features and data from several images. Incorporating medical images from several imaging modalities, such as computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI), into a single set of data is what multi-modal medical image fusion is all about. Better visualization of anatomical structures and clinical conditions is the outcome of this integration, which increases diagnostic accuracy by using the strengths of each modality. In this paper, MRI, CT, and PET scans are used as experimental modalities. This review aims to compare various multi modal medical image approach based on Multi-resolution Singular Value Decomposition (MSVD), Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD). This paper focuses on exploring latest conventional and non-conventional research being done using these domains. It also compares these methods based on various image quality parameters, and various quantitative checks. Based on this comparison, PCA shows the best results in the comparison as the overall visual and parametric quality of fusion results are better than compared methods.

关键词： visualization Magnetic resonance imaging Computed tomography Wavelet analysis Wavelet packets Discrete wavelet transforms Positron emission tomography Medical diagnostic imaging image fusion Principal component analysis

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