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Low-Rank Transformer Adaptation for Arbitrary Style Transfer

Low-Rank Transformer Adaptation for Arbitrary Style Transfer

引用

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

作者： Wenjie Xu Meichen Liu Bihan Wen School of Electrical and Electronic Eng Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Arbitrary style transfer aims to apply artistic characteristics from a style reference to an image while preserving the image’s original content. Although many methods have achieved remarkable results in style transfer, they typically rely on largescale datasets for training, which increases both the cost and complexity of data collection. To address this issue, we propose a Low-rank Transformer Adaptation method for style transfer which leverages the efficiency of low-rank adaptation to reduce the model’s complexity without compromising performance. It not only accelerates the training process but also delivers high-quality image generation with a small-scale dataset. Additionally, we introduce an edge detection loss to enhance the preservation of content outlines further, ensuring that the fine details of the image are maintained during the style transfer process Experimental results demonstrate that our method achieves competitive performance even with significantly less data, and it exhibits superiority in both visual quality and evaluation metrics.

关键词： Training Measurement Adaptation models visualization image synthesis image edge detection Signal processing Transformers Complexity theory Speech processing

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

引用

2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Santhuja, P. Suresh, A. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management. © 2025 ieee.

关键词： Electronic Waste

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MLNet: Mutual Learning Network to Improve Self-Supervised Representation for Fine-Grained visual Recognition

MLNet: Mutual Learning Network to Improve Self-Supervised Re...

引用

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

作者： Peipei Zhao Jiaxuan Wang Zixiang Lu Qiguang Miao School of Computer Science and Technology Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality annotation of fine-grained visual categorization requires extensive professional knowledge, which is time-consuming and laborious. Therefore, learning fine-grained visual representations from a large number of unlabeled images through self-supervised learning has become a popular alternative solution recently. However, the existing self-supervised learning methods are not effective in fine-grained visual categorization since many features helpful for optimizing self-supervised learning objectives are unsuited to characterize the subtle differences in fine-grained viusal recognition. To deal with this issue, we propose a mutual learning network to enhance the model’s attention towards discriminative semantic features. The key idea is to consider semantic consistency between different augmented views within same image and capture discriminative semantic information. For semantic consistency, our research demonstrates that cross-view attention module between different augmented views can guide our model to capture similar semantic features. Based on this, we further build a GradCAM-guided multi-dimension loss that utilize GradCAM to control our model from different dimensions to discriminative semantic information that are beneficial to fine-grained visual recognition. Experiments on CUB-200-2011, Stanford Cars and Aircrafts datasets demonstrate that the mutual learning network outperforms previous self-supervised learning methods in linear probing and image retrieval.

关键词： Training visualization Atmospheric modeling Semantics Self-supervised learning Signal processing Predictive models Feature extraction Speech processing Testing

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Leveraging visual Captions for Enhanced Zero-Shot HOI Detection

Leveraging Visual Captions for Enhanced Zero-Shot HOI Detect...

引用

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

作者： Yanqing Zeng Yunyao Mao Zhenbo Lu Wengang Zhou Houqiang Li EEIS Department University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Zero-shot Human-Object Interaction (HOI) detection aims to identify both seen and unseen HOI categories in an image. Most existing methods rely on semantic knowledge distilled from CLIP to find novel interactions but fail to fully exploit the powerful generalization ability of vision-language models, leading to impaired transferability. In this paper, we introduce a novel framework for zero-shot HOI detection. We first utilize vision-language models (VLMs) to generate visual captions from multiple perspectives, including humans, objects, and environments, to enhance interaction understanding. Then, we propose a multi-modal fusion encoder to fully leverage these visual captions. Additionally, to equip the HOI detector with a thorough consideration of contextual information in the image, we design a novel multi-branch HOI network that aggregates features at the instance, union, and global levels. Experiments on prevalent benchmarks demonstrate that our model achieves promising performance under a variety of zero-shot settings. The source codes are available at https://***/aqingcv/VC-HOI.

关键词： visualization Aggregates Source coding Semantics Detectors Benchmark testing Signal processing Robustness Speech processing Knowledge transfer

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Multi-Source Spatial Knowledge Understanding for Immersive visual Text-to-Speech

Multi-Source Spatial Knowledge Understanding for Immersive V...

引用

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

作者： Shuwei He Rui Liu Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize reverberant speech for the spoken content. Previous works focus on the RGB modality for global environmental modeling, overlooking the potential of multi-source spatial knowledge like depth, speaker position, and environmental semantics. To address these issues, we propose a novel multi-source spatial knowledge understanding scheme for immersive VTTS, termed MS 2 KU-VTTS. Specifically, we first prioritize RGB image as the dominant source and consider depth image, speaker position knowledge from object detection, and Gemini-generated semantic captions as supplementary sources. Afterwards, we propose a serial interaction mechanism to effectively integrate both dominant and supplementary sources. The resulting multi-source knowledge is dynamically integrated based on the respective contributions of each source. This enriched interaction and integration of multi-source spatial knowledge guides the speech generation model, enhancing the immersive speech experience. Experimental results demonstrate that the MS 2 KU-VTTS surpasses existing baselines in generating immersive speech. Demos and code are available at: https://***/AI-S2-Lab/MS2KU-VTTS.

关键词： visualization Codes Semantics Object detection Speech enhancement Signal processing Acoustics Text to speech

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Self-Convolutional Attention-Based Uncertainty-Aware Network for Single-image Super-Resolution

Self-Convolutional Attention-Based Uncertainty-Aware Network...

引用

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

作者： Jinbin Wang Aiping Yang Zihao Wei Qinghua Hu College of Intelligence and Computing Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current super-resolution (SR) algorithms rely heavily on annotated data and often ignore the uncertainty in image degradation and features, limiting their real-world application. We propose an uncertainty-aware SR network using a self-convolutional attention mechanism. Our approach focuses on an SR reconstruction network enhanced by a cross-scale self-convolutional attention mechanism within the Transformer framework, which leverages local regions at various resolutions as convolution kernels to enhance high-frequency details. We also design a heteroscedastic uncertainty loss function to learn pixel and feature uncertainties, guiding the network to improve textures and edges adaptively. Extensive experiments show that our method achieves superior visual reconstruction on standard real-world datasets.

关键词： visualization Uncertainty Attention mechanisms Superresolution Signal processing algorithms Transformers Speech processing image reconstruction Standards Signal resolution

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

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

作者： Xinchen Ma Yue Lu School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloe-mxxxxc/TRIFORMER.

关键词： visualization Technological innovation Noise Graphics processing units Entertainment industry Computer architecture Transformers image restoration Speech processing Remote sensing

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Advanced Embedded Software and Systems using MBD for Electrical Grid Stability

Advanced Embedded Software and Systems using MBD for Electri...

引用

International conference on Communication Systems and Network Technologies (CSNT)

作者： Sachin Umbarkar Sarang Admane Eaton India Innovation Centre (EIIC) Pune Pune India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

This paper describes the advance embedded system and software platform for inspection of grid infrastructure in the electric utilities sector for resilient power supply. A systematic model-based development (MBD) design approach used for plant model development for visual inspection of electric grid and embedded coder used for the controller code generation. The solution for critical applications such as detecting hotspot using embedded infrastructure with cloud/ server-based image processing to achieve the grid stability explained in this paper.

关键词： visualization Embedded systems Systematics Power supplies image processing Power system stability Inspection Stability analysis Power industry Hardware

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FSENet: Frequency Separation Enhancement Network for Super-Resolution

FSENet: Frequency Separation Enhancement Network for Super-R...

引用

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

作者： Liangdong Li Zhihuai Xie School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Developing a lightweight super-resolution network that can achieve high performance has significant value, but remains under-explored. Many existing methods are devoted to generating rich details, thereby improving the visual quality of single image. However, it may introduce errors or redundant information. To overcome these challenges, we propose a lightweight Frequency Separation Enhancement Network (FSENet) that is focus on both high and low frequency information. Furthermore, we introduce a novel Frequency Gradient-based Perceptual Loss (FGPL), specifically designed to enhance the high-frequency components of the super resolution results. The experimental results demonstrate that the proposed FSENet achieves comparable PSNR/SSIM performance with state-of-the-art lightweight SR models, while achieving a processing speed of 107.8 FPS. This performance surpasses that of all previously reported methods.

关键词： Training image quality visualization Superresolution Acoustics Speech processing Signal resolution image reconstruction

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UML: A Unified Multimodal Learning Framework for Cataract Postoperative visual Acuity Prediction with Uncertain Missing Modalities

UML: A Unified Multimodal Learning Framework for Cataract Po...

引用

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

作者： Tongyu Yang Qian Zhou Hua Zou Haifeng Jiang Yong Wang School of Computer Science Wuhan University Wuhan China Aier Eye Hospital Wuhan University Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cataracts are the leading cause of blindness worldwide, with surgery as the only effective treatment. Accurate prediction of Best Corrected visual Acuity (BCVA) is crucial for surgical planning. In this paper, we propose a novel Unified Multimodal Learning (UML) framework for BCVA prediction with uncertain missing modalities. Unlike existing methods that apply generic encoders and overlook critical image variability, UML leverages medical priors to enhance feature extraction through three modules: central concave region enhancement, OCT re-weighting, and multi-scale attention. To manage missing modality uncertainty, we design a missing modality mask fusion network using an attentional mask for unified feature fusion. Additionally, an auxiliary diagnostic text-image contrastive learning task is introduced to further refine image features. UML achieves state-of-the-art performance with a mean absolute error (MAE) of 0.0457 and 96.25% predictions fall within an error of ± 0.10 LogMAR. Codes are available at https://***/yty9941/Eyer-BCVA

关键词： Cataracts visualization Uncertainty Unified modeling language Surgery Signal processing Feature extraction Planning Speech processing Medical diagnostic imaging

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