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共 18110 条记录，以下是301-310 订阅

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GCAT: Gated Convolutional Attention Transformer for Efficient Image Super-Resolution

GCAT: Gated Convolutional Attention Transformer for Efficien...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhijian Wu Kaiyi Feng Dingjiang Huang School of Data Science and Engineering East China Normal University Shanghai China School of Control and Computer Engineering North China Electric Power University Hebei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, Transformer-based methods have achieved impressive performance in many computer vision tasks (e.g., image super-resolution (SR)) due to the advantages of long-range modeling. However, the computational cost requirement renders these methods unsuitable on resource-constrain devices, especially for image SR tasks involving high-resolution images. In this paper, we propose a concise and effective Gated Convolutional Attention Unit (GCAU) that uses cheap convolutional operations. Specifically, GCAU consists of Convolutional Transposed Attention (CTA) and Locally-enhanced Gating (LeG) in parallel. The former allows for efficient modeling of the global relational interactions by calculating cross-covariance across channels dimension, while the latter controls the information flow from the former directing the network to focus on more refined image attributes. Without bells and whistles, we present a simple SR Transformer GCAT by cascading the GCAUs. Extensive experimental results demonstrate that our GCAT achieves state-of-the-art performance among the existing efficient SR methods with significantly less complexity. Especially, GCAT is on average 5× faster than SwinIR-light with comparable performance.

关键词： Legged locomotion Performance evaluation Computer vision Convolution Computational modeling Superresolution Logic gates Transformers Speech processing signal resolution

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Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift

Disentangling Hierarchical Features for Anomalous Sound Dete...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jian Guan Jiantong Tian Qiaoxi Zhu Feiyang Xiao Hejing Zhang Xubo Liu Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University Harbin China Acoustics Lab University of Technology Sydney Ultimo NSW Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Anomalous sound detection (ASD) encounters difficulties with domain shift, where the sounds of machines in target domains differ significantly from those in source domains due to varying operating conditions. Existing methods typically employ domain classifiers to enhance detection performance, but they often overlook the influence of domain-unrelated information. This oversight can hinder the model’s ability to clearly distinguish between domains, thereby weakening its capacity to differentiate normal from abnormal sounds. In this paper, we propose a Gradient Reversal-based Hierarchical feature Disentanglement (GRHD) method to address the above challenge. GRHD uses gradient reversal to separate domain-related features from domain-unrelated ones, resulting in more robust feature representations. Additionally, the method employs a hierarchical structure to guide the learning of fine-grained, domain-specific features by leveraging available metadata, such as section IDs and machine sound attributes. Experimental results on the DCASE 2022 Challenge Task 2 dataset demonstrate that the proposed method significantly improves ASD performance under domain shift.

关键词： Metadata signal processing Feature extraction Acoustics Speech processing

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ICAA-Mamba: vision Mamba for Image Color Aesthetics Assessment

ICAA-Mamba: Vision Mamba for Image Color Aesthetics Assessme...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Qinghao Xie Wenchao Jiang Zhihua Wang Qiuping Jiang School of Computer Science Guangdong University of Technology Guangzhou China Department of Engineering Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Ningbo University Ningbo China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Image Color Aesthetics Assessment (ICAA) focuses on evaluating the aesthetic quality of color composition within images. This task involves analyzing and quantifying the visual appeal of color arrangements, taking into account factors such as harmony, contrast, and balance, to provide an objective assessment of color aesthetics. In this paper, we investigate the application of the State Space Model (Mamba) to the ICAA task, with a focus on exploring the perceptual capabilities of vision Mamba. To this end, we propose a novel framework that employs Mamba as the backbone to extract informative patterns from images, leveraging its global receptive field and linear complexity with respect to input length. Instead of relying solely on a final-layer feature, followed by fully connected layers for quality prediction, we exploit a comprehensive set of multi-scale features, thereby constructing a richer global representation of color information. To fuse these multi-scale features effectively, we introduce a Weighted Feature Fusion Module (WFFM), which adaptively assigns weights to emphasize salient color-related information. We conduct extensive experiments on two benchmark datasets, ICAA17K and SPAQ, successfully demonstrating its effectiveness for the ICAA task. The code is available at https://***/qinghaoya/icaa-mamba.

关键词： Deep learning Visualization Codes Image color analysis Fuses signal processing Feature extraction Complexity theory Data mining Speech processing

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FashionFAE: Fine-grained Attributes Enhanced Fashion vision-Language Pre-training

FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiale Huang Dehong Gao Jinxia Zhang Zechao Zhan Yang Hu Xin Wang Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China Northwestern Polytechnical University China Binjiang Institute of Artificial Intelligence ZJUT Hangzhou China Advanced Ocean Institute of Southeast University Nantong China Alibaba Group China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large-scale vision-Language Pre-training (VLP) has demonstrated remarkable success in the general domain. However, in the fashion domain, items are distinguished by fine-grained attributes such as texture and material, which are crucial for tasks such as retrieval. Existing models often fail to take advantage of these fine-grained attributes from both text and image modalities. To address the above issue, we propose a novel approach for the fashion domain, Fine-grained Attributes Enhanced VLP (FashionFAE), which focuses on the detailed characteristics of the fashion data. An attribute-emphasized text prediction task is proposed to predict fine-grained attributes of the items. This forces the model to focus on the salient attributes from the text modality. In addition, a novel attribute-promoted image reconstruction task is proposed, which further enhances the fine-grained ability of the model by leveraging the representative attributes from the image modality. Extensive experiments show that FashionFAE outperforms State-Of-The-Art (SOTA) methods, achieving 2.9% and 5.2% improvements in retrieval on sub-test set and full test set, respectively, and an average improvement of 1.6% in recognition tasks.

关键词： signal processing Predictive models Acoustics Data mining Speech processing Image reconstruction

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AI-Driven robotic Disassembly: Harnessing IoT and Data Collection for Sustainable E-Waste Solutions

AI-Driven Robotic Disassembly: Harnessing IoT and Data Colle...

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Control, Automation, and Instrumentation (IC2AI), international conference on

作者： Soufiane Ameur Zineb Hidila Mohamed Tabaa Richard Bearee Mohamed Hamlich CCPS Laboratory ENSAM University of Hassan II Casablanca Morocco Multidisciplinary Laboratory of Research and Innovation (LPRI) Moroccan School of Engi-neering Sciences (EMSI) Casablanca Morocco Arts et Metiers Institute of Technology LISPEN HESAM Universite Lille France

ISBN: (数字)9798331529604

ISBN: (纸本)9798331529611

The escalating environmental impact of electronic waste poses substantial challenges for sustainable waste management and material reclamation. This study proposes an intelligent robotic disassembly station that integrates Artificial Intelligence (AI) and Internet of Things (IoT) to optimize autonomous e-waste data collection and disassembly processing. The station incorporates a conveyor belt, metal sensor, and a camera attached to a robot to detect and categorize materials in real-time. The idea is to construct a learning-based dataset, enabling the system to recognize and optimize disassembly procedures for unknown waste. Over time, the system enhances efficiency by predicting the disassembly pathway for similar products, reducing time and energy expenditure. The integration of IoT enhances communication between sensors and cloud platforms for real-time monitoring and data storage, ensuring the robot recognizes its environment. Experimental findings demonstrate a significant enhancement in material recovery rates and its precision, contributing to more environmental Solutions.

关键词： robot vision systems Metals Electronic waste Data collection robot sensing systems Cameras Real-time systems Internet of Things Artificial intelligence robots

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VITRO: Vocabulary Inversion for Time-series Representation Optimization

VITRO: Vocabulary Inversion for Time-series Representation O...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Filippos Bellos Nam H. Nguyen Jason J. Corso University of Michigan Ann Arbor MI USA Capital One McLean VA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Although LLMs have demonstrated remarkable capabilities in processing and generating textual data, their pretrained vocabularies are ill-suited for capturing the nuanced temporal dynamics and patterns inherent in time series. The discrete, symbolic nature of natural language tokens, which these vocabularies are designed to represent, does not align well with the continuous, numerical nature of time series data. To address this fundamental limitation, we propose VITRO. Our method adapts textual inversion optimization from the vision-language domain in order to learn a new time series per-dataset vocabulary that bridges the gap between the discrete, semantic nature of natural language and the continuous, numerical nature of time series data. We show that learnable time series-specific pseudo-word embeddings represent time series data better than existing general language model vocabularies, with VITRO-enhanced methods achieving state-of-the-art performance in long-term forecasting across most datasets.

关键词： Vocabulary Time series analysis Natural languages Semantics signal processing Predictive models Numerical models Speech processing Forecasting Optimization

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Egocentric Speaker Diarization with vision-Guided Clustering and Adaptive Speech Re-detection

Egocentric Speaker Diarization with Vision-Guided Clustering...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： He Huang Haoyuan Yu Daibo Liu Haowen Chen Minjie Cai Hunan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker diarization aims to identify "who spoke when" in multi-person conversational scenarios. State-of-the-art audio-only diarization methods divide the task into multi-stages of speech segmentation, neural speaker embedding and unsupervised clustering. Egocentric speaker diarization (i.e., diarization in egocentric videos) is characterized by natural conversational scenarios involving a variety of noisy backgrounds, changing sound levels and overlapping speech. These cause difficulty in predicting the number of speakers from audio input, and heavily influence the performance of audio-based clustering. Although audio-visual modeling has been studied recently to enhance speaker diarization, unreliable visual information in egocentric videos may even degrade the performance. In this work, we propose a unified audio-visual diarization framework by incorporating visual guidance into the audio-only diarization pipeline. In addition, we also propose an adaptive speech re-detection strategy to detect and assign speaker identity to the mistakenly undetected audio segments. Experiments on the Ego4D dataset show that our method achieves state-of-the-art diarization performance in challenging egocentric scenarios. The code and model weights are available at https://***/YellowRiver2001/EgoDiarization.

关键词： Visualization Adaptation models Codes Pipelines signal processing algorithms Clustering algorithms signal processing Noise measurement Speech processing Videos

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Efficient Large-Scale Scene Point Cloud Upsampling with Implicit Neural Networks and Spatial Hashing

Efficient Large-Scale Scene Point Cloud Upsampling with Impl...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhiyong Zhang Yunrui Zhu Ruyu Liu Xu Cheng Jianhua Zhang Xiufeng Liu School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Point cloud upsampling is a critical challenge in 3D vision, particularly for large-scale, real-world data. We propose ASFNet, a novel implicit neural network-based approach that uniquely combines adaptive spatial feature representation with efficient spatial hashing. This method significantly improves both upsampling quality and computational efficiency. ASFNet first encodes the point cloud as an implicit surface, employing dynamic search and spatial hashing to optimize query point locations rapidly. This approach creates a uniform, continuous field around surfaces, enabling high-fidelity upsampling. Experiments on benchmark datasets, including Oakland 3D dataset and VMR-Oakland-v2, demonstrate ASFNet’s superiority. Our method achieves state-of-the-art performance with a Chamfer Distance of 5.559 × 10 –3 on Oakland 3D dataset, while reducing processing time by up to 80% compared to existing methods. On the challenging Oakland 3D dataset, ASFNet completes upsampling in just 150 seconds. These results underscore ASFNet’s potential to advance real-time 3D vision applications in areas such as autonomous navigation and augmented reality.

关键词： Point cloud compression Technological innovation Solid modeling Three-dimensional displays Neural networks signal processing Neural radiance field Real-time systems Speech processing Surface treatment

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Chaos Theory Based Gravitational Search Algorithm For Medical Image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： signal to noise ratio

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Artificial Intelligence Algorithms for robotic Harvesting of Agricultural Produce

Artificial Intelligence Algorithms for Robotic Harvesting of...

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AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Nilesh R. Kolhalkar Anupama A. Pandit Shridhar Ashok Kedar G. Yedukondalu Department of Mechanical Engineering MKSSS's Cummins College of Engg. For Women Pune MH India Department of Computer Science & Engg. Defence Institute of Advanced Technology (D.I.A.T) Pune Pune MH India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Vijayawada A.P. India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, Artificial Intelligence, and Machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations.

关键词： Accuracy Costs Automation Machine vision Pipelines Lighting Training data robot sensing systems Prediction algorithms robots

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