ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献

A Convex Formulation for the Robust Estimation of Multivariate Exponential Power Models 多元指数幂模型鲁棒估计的凸公式

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747354

N. Ouzir, J. Pesquet, F. Pascal

引用次数: 0

Learning to Fuse Heterogeneous Features for Low-Light Image Enhancement 学习融合异构特征的弱光图像增强

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9746255

Zhenyu Tang, Long Ma, Xiaoke Shang, Xin Fan

{"title":"Learning to Fuse Heterogeneous Features for Low-Light Image Enhancement","authors":"Zhenyu Tang, Long Ma, Xiaoke Shang, Xin Fan","doi":"10.1109/icassp43922.2022.9746255","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9746255","url":null,"abstract":"To see clearly in low-light scenarios, a series of learning-based techniques have been developed to improve visual quality. However, due to the absence of semantic-level features, the existing methods are perhaps less effective on semantic-oriented visual analysis tasks (e.g., saliency detection). To break down the limitation, we propose a new classification-driven enhancement method with heterogeneous feature fusion. Specifically, we construct a new low-light image enhancement network by integrating features acquired from the pre-trained classification network. Then, to better exploit the semantic-level information, we establish a Heterogeneous Feature Fusion (HF2) operation with channel-and-spatial attention to strength the effects of cross-domain features. HF2 acts on not only the fusion between classification and encoded features but also the fusion between encoded and decoded features. Extensive experiments are conducted to indicate our superiority against other state-of-the-art methods. The application on saliency detection further reveals our effectiveness in settling the semantic-oriented visual tasks.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Connecting Targets via Latent Topics And Contrastive Learning: A Unified Framework For Robust Zero-Shot and Few-Shot Stance Detection 通过潜在主题和对比学习连接目标:一种鲁棒零弹和少弹姿态检测的统一框架

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9746739

R. Liu, Zheng Lin, Peng Fu, Yuanxin Liu, Weiping Wang

引用次数: 5

Deterministic Transform Based Weight Matrices for Neural Networks 基于确定性变换的神经网络权重矩阵

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747256

Pol Grau Jurado, Xinyue Liang, S. Chatterjee

引用次数: 0

Real-World On-Board Uav Audio Data Set For Propeller Anomalies 真实世界的机载无人机音频数据集螺旋桨异常

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/ICASSP43922.2022.9747789

Sai Srinadhu Katta, Kide Vuojärvi, S. Nandyala, Ulla-Maria Kovalainen, Lauren Baddeley

{"title":"Real-World On-Board Uav Audio Data Set For Propeller Anomalies","authors":"Sai Srinadhu Katta, Kide Vuojärvi, S. Nandyala, Ulla-Maria Kovalainen, Lauren Baddeley","doi":"10.1109/ICASSP43922.2022.9747789","DOIUrl":"https://doi.org/10.1109/ICASSP43922.2022.9747789","url":null,"abstract":"Detecting propeller damage in Unmanned Aerial Vehicles (UAV) is a crucial step in ensuring their operational resilience and safety. In this work, we present a novel real-world audio data set of propeller anomalies, and use several deep learning models to classify the damage. This data set consists of more than 5 hours of audio recordings, covering all configurations of intact and broken propellers in a UAV quadcopter. A microphone array was mounted onto a UAV, and numerous autonomous indoor missions were flown. Our on-board setup has provided clean audio recordings containing little background noise. We have developed classification models for this data set, using different deep learning architectures: Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformer Encoder (TrEnc). We conclude that the TrEnc outperforms other architectures, having 11k parameters, .57M Flops, 98.30% accuracy, .98 precision, and .98 recall. Finally, we make our data set publicly available here⊙.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115447060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

FDSNeT: An Accurate Real-Time Surface Defect Segmentation Network FDSNeT:一个精确的实时表面缺陷分割网络

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747311

Jian Zhang, Runwei Ding, Miaoju Ban, Tianyu Guo

{"title":"FDSNeT: An Accurate Real-Time Surface Defect Segmentation Network","authors":"Jian Zhang, Runwei Ding, Miaoju Ban, Tianyu Guo","doi":"10.1109/icassp43922.2022.9747311","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9747311","url":null,"abstract":"Surface defect detection is a common task for industrial quality control, which increasingly requires accuracy and real-time ability. However, the current segmentation networks are not effective in dealing with defect boundary details, local similarity of different defects and low contrast between defect and background. To this end, we propose a real-time surface defect segmentation network (FDSNet) based on two-branch architecture, in which two corresponding auxiliary tasks are introduced to encode more boundary details and semantic context. To handle the local similarity problem of different surface defects, we propose a Global Context Upsampling (GCU) module by capturing long-range context from multi-scales. Moreover, we present a representative Mobile phone screen Surface Defect (MSD) segmentation dataset to alleviate the lack of dataset in this field. Experiments on NEU-Seg, Magnetic-tile-defect-datasets and MSD dataset show that the proposed FDSNet achieves promising trade-off between accuracy and inference speed. The dataset and code are available at https://github.com/jianzhang96/fdsnet.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123064645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Learning Deep Pathological Features for WSI-Level Cervical Cancer Grading 了解wsi级宫颈癌分级的深层病理特征

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/ICASSP43922.2022.9747112

Ruixiang Geng, Qing Liu, Shuo Feng, Yixiong Liang

引用次数: 3

Contrastive Translation Learning For Medical Image Segmentation 用于医学图像分割的对比翻译学习

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747097

Wankang Zeng, Wenkang Fan, Dongfang Shen, Yinran Chen, Xióngbiao Luó

引用次数: 0

FlowDT: A Flow-Aware Digital Twin for Computer Networks 面向计算机网络的流感知数字孪生

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9746953

Miquel Ferriol Galmés, Xiangle Cheng, Xiang Shi, Shihan Xiao, P. Barlet-Ros, A. Cabellos-Aparicio

{"title":"FlowDT: A Flow-Aware Digital Twin for Computer Networks","authors":"Miquel Ferriol Galmés, Xiangle Cheng, Xiang Shi, Shihan Xiao, P. Barlet-Ros, A. Cabellos-Aparicio","doi":"10.1109/icassp43922.2022.9746953","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9746953","url":null,"abstract":"Network modeling is an essential tool for network planning and management. It allows network administrators to explore the performance of new protocols, mechanisms, or optimal configurations without the need for testing them in real production networks. Recently, Graph Neural Networks (GNNs) have emerged as a practical solution to produce network models that can learn and extract complex patterns from real data without making any assumptions. However, state-of-the-art GNN-based network models only work with traffic matrices, this is a very coarse and simplified representation of network traffic. Although this assumption has shown to work well in certain use-cases, it is a limiting factor because, in practice, networks operate with flows. In this paper, we present FlowDT a new DL-based solution designed to model computer networks at the fine-grained flow level. In our evaluation, we show how FlowDT can accurately predict relevant per-flow performance metrics with an error of 3.5%, FlowDT’s performance is also benchmarked against vanilla DL models as well as with Queuing Theory.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Two-Stage Beam Training and Channel Estimation for Ris-Aided Mmwave Systems Via Fast Alternating Least Squares 基于快速交替最小二乘的ris辅助毫米波系统有效两级波束训练和信道估计

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9746094

Hyeonjin Chung, Sunwoo Kim

引用次数: 0