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Volatile MAB-based Configuration Selection for Offloading Video Analytics Tasks to Edges

Volatile MAB-based Configuration Selection for Offloading Vi...

引用

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

作者： Yu Liang Sheng Zhang Jie Wu School of Computer and Electronic Information and the School of Artificial Intelligence Nanjing Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Networked Computing Temple University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The demand for video analytics is increasing rapidly. Due to the limited computational and network resources on edge servers, adjusting video configurations such as resolution and frame rate has become an effective strategy to reduce computational and transmission costs. However, this can also compromise detection accuracy, necessitating a balance between resource consumption and analytics accuracy. Also, the dynamic availability of edge servers and variability in their energy consumption further complicates making offloading decisions and configuration selection. In this paper, we first model the problem as a mixed planning program. Then we propose a volatile MAB-based configuration selection algorithm, VACS, which aims to maximize video analytics accuracy while reducing the overall energy consumption. Rigorous proof measures the gap between online decisions and the optimum. Extensive experiments validate the effectiveness of VACS.

关键词： Energy consumption Accuracy visual analytics image edge detection Heuristic algorithms Signal processing algorithms Signal processing Planning Servers Speech processing

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Toward visual Pronunciation Learning: A Speech-to-Articulatory Animation Pipeline Leveraging wav2vec 2.0 and rtMRI Landmarks

Toward Visual Pronunciation Learning: A Speech-to-Articulato...

引用

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

作者： Mushaffa Rasyid Ridha Shinobu Hasegawa Sakriani Sakti Grad. School of Adv. Science & Tech. Japan Adv. Inst. of Science & Tech. Nomi Japan Center of IDER Japan Adv. Inst. of Science & Tech. Nomi Japan Grad. School of Science & Tech. Nara Inst. of Science & Tech. Ikoma Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Most computer-assisted pronunciation training (CAPT) systems for second language (L2) learners focus on detecting mispronunciation based on predefined phonemes and assigning pronunciation scores. However, these systems often lack visual feedback or detailed corrective guidance, limiting learners’ opportunities for significant improvement. This paper presents a key advance toward developing a CAPT system that offers detailed visual feedback on articulatory movements using real-time magnetic resonance imaging (rtMRI) articulatory landmarks. The limited availability of paired speech and articulatory landmark data, typically involving only a few speakers, poses a challenge for generalizing across diverse speech patterns. To address this, we propose leveraging pretrained wav2vec 2.0 embeddings, fine-tuned to generate articulatory contours mapped to xy coordinates based on rtMRI landmark data. As evaluated with the rtMRI USC-TIMIT dataset, our system effectively reconstructs visual articulatory movements from speech, marking a significant step toward enhanced visual pronunciation learning.

关键词： Training visualization Limiting Magnetic resonance imaging Pipelines Neural networks Speech enhancement Feature extraction Real-time systems image reconstruction

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BIAWDiff: Enhancing Low-Light images with Bio-Inspired Attention and Wavelet Diffusion

BIAWDiff: Enhancing Low-Light Images with Bio-Inspired Atten...

引用

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

作者： Zeyu Li Sheng Yang Hanxiang Yang Xiongxin Tang Fengge Wu Fanjiang Xu Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light image enhancement aims to improve visual quality under challenging lighting conditions while preserving details and color fidelity. Existing traditional algorithms and deep learning approaches, often struggle with balancing brightness enhancement and detail preservation, leading to issues such as overexposure, artifacts, and loss of high-frequency details. To address these challenges, we propose a novel method, Bio-Inspired Attention and Wavelet Diffusion (BIAWDiff), that integrates Retinex theory with bio-inspired attention and wavelet-based diffusion models to enhance low-light images. BIAWDiff consists of three key modules: the Initial Light Restoration (ILR) module for brightness enhancement and noise reduction, the Rod Cell-Inspired Attention Refinement (RCAR) module for luminance refinement, and the Detail Refinement (DR) module for restoring high-frequency details. Experimental results demonstrate that BIAWDiff outperforms existing techniques, achieving superior results in brightness enhancement, noise reduction, and detail preservation, with an average PSNR increase of 5.1% and SSIM improvement of 3.2% on paired datasets, and a reduction in NIQE and BRISQUE by 7.4% and 8.6% on unpaired datasets.

关键词： visualization Brightness Noise reduction Refining Signal processing algorithms Lighting Speech enhancement Signal processing image restoration Biomedical imaging

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Knowledge-Guided Prompt Learning for Deepfake Facial image Detection

Knowledge-Guided Prompt Learning for Deepfake Facial Image D...

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

作者： Hao Wang Cheng Deng Zhidong Zhao School of Cyberspace Security Hangzhou Dianzi University Hangzhou China School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent generative models demonstrate impressive performance on synthesizing photographic images, which makes humans hardly to distinguish them from pristine ones, especially on realistic-looking synthetic facial images. Previous works mostly focus on mining discriminative artifacts from vast amount of visual data. However, they usually lack the exploration of prior knowledge and rarely pay attention to the domain shift between training categories (e.g., natural and indoor objects) and testing ones (e.g., fine-grained human facial images), resulting in unsatisfactory detection performance. To address these issues, we propose a novel knowledge-guided prompt learning method for deepfake facial image detection. Specifically, we retrieve forgery-related prompts from large language models as expert knowledge to guide the optimization of learnable prompts. Besides, we elaborate test-time prompt tuning to alleviate the domain shift, achieving significant performance improvement and boosting the application in real-world scenarios. Extensive experiments on DeepFakeFaceForensics dataset show that our proposed approach notably outperforms state-of-the-art methods.

关键词： Training Representation learning Deepfakes visualization Large language models Signal processing Speech processing Tuning Optimization Testing

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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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Lightweight Low-Light image Enhancement Model Training and Design Considerations

Lightweight Low-Light Image Enhancement Model Training and D...

引用

Annual ieee Systems conference

作者： Hajira Saleem Reza Malekian Hussan Munir Dept. of computer science Malmö University Malmö Sweden Internet of Things and People Research Centre Malmö University Malmö Sweden

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Low-light conditions significantly degrade the performance of RGB cameras, particularly in applications like visual odometry estimation for autonomous vehicles, where feature visibility is critical for accurate navigation. Traditional image enhancement techniques often require manual tuning and struggle in extremely dark environments. However, deep-learning-based methods, though promising, typically demand high computational resources, making them unsuitable for real-time applications. This paper presents top low-light image enhancement (LLIE) models obtained through our experimentation with designing models to improve the visibility of features crucial for odometry tasks while minimizing computational overhead. We explore design and training strategies for real-time low-light enhancement using U-Net, a CNN architecture, alongside other CNN models with residual blocks and attention mechanisms. Through experimentation with two datasets-LOL-v2 and KITTI datasets, we demonstrate that our models offer significant improvements in feature visibility without compromising real-time performance. We also report that training the model on image patches reduces Graphics processing unit (GPU) memory usage and improves image enhancement quality, though training on full images may sometimes be necessary. Compared to a high-performing baseline model, our approach-despite yielding lower image quality is better suited for real-time applications. Some use cases, such as robotic navigation, can benefit from our lightweight model, where high-resolution details are less critical, as the focus is on real-time performance and general feature visibility. Additionally, we find that using U-Net without residual blocks and attention mechanisms results in degraded image quality. Future work will focus on transfer learning our model for odometryspecific image enhancement and integrating it into autonomous localization systems to optimize computational efficiency and enhance performance.

关键词： Training Location awareness image quality Attention mechanisms Navigation Computational modeling Graphics processing units Real-time systems Odometry image enhancement

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Multi-View image Enhancement Inconsistency Decoupling Guided 3D Gaussian Splatting

Multi-View Image Enhancement Inconsistency Decoupling Guided...

引用

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

作者： Lu Xiao Jiahao Wu Zhanke Wang Guanhua Wu Runling Liu Zhiyan Wang Ronggang Wang Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology Shenzhen Graduate School Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D Gaussian Splatting (3DGS) has recently made breakthrough progress in radiance field reconstruction but struggles with multi-view inconsistency. Modern cameras often apply tailored enhancements to each view when capturing multi-view images. While this improves individual image quality, it inevitably disrupts the multi-view consistency assumption, particularly in challenging conditions like nighttime scenes. This disrupts 3DGS’s ability to accurately reconstruct the scene, resulting in the emergence of artifacts such as "floaters". To tackle this challenge, we propose equipping each training view with a 4D bilateral affine grid to decouple the inconsistencies across multi-view images, guiding more accurate 3DGS reconstruction. To solve the problem of less visually appealing renderings caused by the absence of certain enhancements, our 4D bilateral affine grid also serves a dual purpose: it propagates enhanced features from a single edited image throughout the entire 3D scene, allowing for comprehensive 3D editing with just one image. Experiments demonstrate that our method not only effectively decouples inconsistencies in multi-view images, enabling more precise 3DGS reconstruction, but also facilitates refined 3D editing, further improving the visual quality of otherwise monotonous scenes.

关键词： Training Photography image quality visualization Three-dimensional displays Accuracy Speech enhancement Signal processing Rendering (computer graphics) image reconstruction

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RETINOSCAN: Retinal image-Based Diabetic Retinopathy Detection

RETINOSCAN: Retinal Image-Based Diabetic Retinopathy Detecti...

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Integrated Circuits and Communication Systems (ICICACS), ieee International conference on

作者： Nanditha Krishna Ashrita Dikshit V A Kavana G R Nishita S Rakshit Preran B Department of Medical Electronics Engineering Center for Medical Electronics and Computational Intelligence Dayananda Sagar College of Engineering Bengaluru India Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Diabetic Retinopathy (DR) is a leading cause of vision impairment worldwide, necessitating early detection and accurate diagnosis. This study presents a reliable system that leverages advanced image processing and deep learning techniques to identify and classify DR stages. MATLAB is utilized for fundus image preprocessing, integrating a pre-trained Inception-v3 model for feature extraction and stage-wise classification. A web-based graphical user interface enhances clinical applicability. Deployment through MATLAB Compiler and Docker ensures real-world accessibility. The model, trained and validated on a comprehensive retinal image dataset, demonstrates high reliability and accuracy. This system has the potential to improve patient outcomes and establish new standards in automated retinal disease diagnostics.

关键词： Deep learning Diabetic retinopathy Accuracy visual impairment Retina Mathematical models MATLAB Integrated circuit modeling Graphical user interfaces Diseases

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Collaborative Dual-Branch Spatial-Frequency Enhancement Network for Low-Light images

Collaborative Dual-Branch Spatial-Frequency Enhancement Netw...

引用

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

作者： Tao He Tiecheng Song Yin Liu Feng Yang Ruiyuan Chen Zhixin Li School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light images are commonly present due to imaging factors such as insufficient light, night shooting and back lit. Existing low-light image enhancement (LLIE) methods typically rely on a low-light input image for enhancement, which seldom leverage information contained in its high-light counterpart to restore image structures and handle complex lighting, leading to unsatisfactory image quality. In view of this, in this paper we propose a Collaborative Dual-Branch Spatial-Frequency Enhancement Network (CDSE-Net). Specifically, we apply the inversion operation to low-light images to self-generate high-light images and build a collaborative dual-branch network which enhances images sequentially in spatial and frequency domains. In the spatial domain, we leverage adaptive curve estimation and multi-direction convolutions to restore lightness and structure information, respectively. In the frequency domain, we perform amplitude interactions on dual-branch images and 1×1 convolution on phase features to adjust image lightness and structures, respectively. Finally, we introduce an illumination-aware attention module to fuse two branches. Experiments on several widely used datasets quantitatively and qualitatively demonstrate the advantages of our network over state-of-the-art methods for LLIE.

关键词： image quality Convolution Fuses Frequency-domain analysis Collaboration Lighting Imaging Speech enhancement image restoration image reconstruction

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Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on Viseme Decoding

Towards Dynamic Neural Communication and Speech Neuroprosthe...

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

作者： Ji-Ha Park Seo-Hyun Lee Soowon Kim Seong-Whan Lee Dept. of Artificial Intelligence Korea University Seoul Republic of Korea Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools for general users. Although neural signals contain various information on speech intentions, movements, and phonetic details, generating informative outputs from them remains challenging, with mostly focusing on decoding short intentions or producing fragmented outputs. In this study, we developed a diffusion model-based framework to decode visual speech intentions from speech-related non-invasive brain signals, to facilitate face-to-face neural communication. We designed an experiment to consolidate various phonemes to train visemes of each phoneme, aiming to learn the representation of corresponding lip formations from neural signals. By decoding visemes from both isolated trials and continuous sentences, we successfully reconstructed coherent lip movements, effectively bridging the gap between brain signals and dynamic visual interfaces. The results highlight the potential of viseme decoding and talking face reconstruction from human neural signals, marking a significant step toward dynamic neural communication systems and speech neuroprosthesis for patients.

关键词： visualization Communication systems Lips Neuroprostheses Signal processing Phonetics Brain modeling Decoding Speech processing image reconstruction

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