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检索条件"主题词=Encoder-decoder network"

共 181 条记录，以下是121-130 订阅

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MULTIPLE AUXILIARY networkS FOR SINGLE BLIND IMAGE DEBLURRING

MULTIPLE AUXILIARY NETWORKS FOR SINGLE BLIND IMAGE DEBLURRIN...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Li, Chen Wang, Qi Liu, Shaoteng Li, Xuelong Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781728176055

Single blind image deblurring caused by a combination of multiple factors has been one of the most challenging visual tasks. Recently, many essential methods of this task are based on deep learning networks and have achieved high performance. However, most of them only apply norm pixel-wise L1-loss function as the guide of training, which is not suitable or effective enough. In this paper, we propose Multiple Auxiliary networks (MANet) for single blind image deblurring to assist norm L1-loss function and enhance the quality of the deblurring image. The main branch of our MANet is an encoder-decoder structure made up of residual blocks, and the three auxiliary branches are the edge prediction branch, the multi-scale refinement branch, and the perceptual loss branch. The experimental results demonstrate that the proposed MANet can obtain better deblurring performance with more details than state-of-the-art methods. The code is released at ***/ZERO2ER0/MANet.

关键词： Single blind image deblurring encoder-decoder network Auxiliary branches network

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DOES SPEECH ENHANCEMENTWORK WITH END-TO-END ASR OBJECTIVES?: EXPERIMENTAL ANALYSIS OF MULTICHANNEL END-TO-END ASR

DOES SPEECH ENHANCEMENTWORK WITH END-TO-END ASR OBJECTIVES?:...

引用

27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Ochiai, Tsubasa Watanabe, Shinji Katagiri, Shigeru Doshisha Univ Kyoto Japan Mitsubishi Elect Res Labs Amagasaki Hyogo Japan

ISBN: (纸本)9781509063413

Recently we proposed a novel multichannel end-to-end speech recognition architecture that integrates the components of multichannel speech enhancement and speech recognition into a single neural-network-based architecture and demonstrated its fundamental utility for automatic speech recognition (ASR). However, the behavior of the proposed integrated system remains insufficiently clarified. An open question is whether the speech enhancement component really gains speech enhancement (noise suppression) ability, because it is optimized based on end-to-end ASR objectives instead of speech enhancement objectives. In this paper, we solve this question by conducting systematic evaluation experiments using the CHiME-4 corpus. We first show that the integrated end-to-end architecture successfully obtains adequate speech enhancement ability that is superior to that of a conventional alternative (a delay-and-sum beamformer) by observing two signal-level measures: the signal-to-distortion ratio and the perceptual evaluation of speech quality. Our findings suggest that to further increase the performances of an integrated system, we must boost the power of the latter-stage speech recognition component. However, an insufficient amount of multichannel noisy speech data is available. Based on these situations, we next investigate the effect of using a large amount of single-channel clean speech data, e.g., the WSJ corpus, for additional training of the speech recognition component. We also show that our approach with clean speech significantly improves the total performance of multichannel end-to-end architecture in the multichannel noisy ASR tasks.

关键词： Multichannel end-to-end automatic speech recognition neural beamformer encoder-decoder network

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Semantic Segmentation on Multi-Spectral Images 29

Semantic Segmentation on Multi-Spectral Images

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29th IEEE Conference on Signal Processing and Communications Applications (SIU)

作者： Aslantas, Veysel Toprak, Ahmet Nusret Elmaci, Mehmet Erciyes Univ Bilgisayar Muhendisligi Bolumu Kayseri Turkey

ISBN: (纸本)9781665436496

Semantic segmentation is considered to be one of the basic steps in understanding image content. For semantic segmentation, if multi-spectral images are used together with color images, more successful results are obtained due to complementary information obtained from multi-spectral images. In this paper, a semantic segmentation method was developed in which the images obtained from CCD and thermal sensors were used together. In the proposed method, convolutional neural networks were used in encoder-decoder architecture. The experiments carried out show that the developed method produces better numerical and visual results than the works published in the literature.

关键词： semantic segmentation deep learning convolutional neural network encoder-decoder network

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A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation 6th

A Two-Stage Cascade Model with Variational Autoencoders and ...

引用

6th International MICCAI Brain-Lesion Workshop (BrainLes)

作者： Lyu, Chenggang Shu, Hai NYU Dept Biostat Sch Global Publ Hlth New York NY 10003 USA

ISBN: (纸本)9783030720834;9783030720841

Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. Variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorff distance (95%) of 4.953, 6.299, 23.608 for the whole tumor, tumor core, and enhancing tumor, respectively. The corresponding results on the BraTS 2020 testing dataset are 0.8729, 0.8357, and 0.8205 for Dice score, and 11.4288, 19.9690, and 15.6711 for Hausdorff distance. The code is publicly available at https://***/shu-hai/two-stage-VAE-Attention-gate-BraTS2020.

关键词： Attention gate Brain tumor segmentation encoder-decoder network Variational autoencoder

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GENERATING AESTHETIC BASED CRITIQUE FOR PHOTOGRAPHS

GENERATING AESTHETIC BASED CRITIQUE FOR PHOTOGRAPHS

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IEEE International Conference on Image Processing (ICIP)

作者： Yeo, Yong-Yaw See, John Wong, Lai-Kuan Goh, Hui-Ngo Multimedia Univ Fac Comp & Informat Cyberjaya Malaysia Heriot Watt Univ Sch Math & Comp Sci Putrajaya Malaysia

ISBN: (纸本)9781665441155

The recent surge in deep learning methods across multiple modalities has resulted in an increased interest in image captioning. Most advances in image captioning are still focused on the generation of factual-centric captions, which mainly describe the contents of an image. However, generating captions to provide a meaningful and opinionated critique of photographs is less studied. This paper presents a framework for leveraging aesthetic features encoded from an image aesthetic scorer, to synthesize human-like textual critique via a sequence decoder. Experiments on a large-scale dataset show that the proposed method is capable of producing promising results on relevant metrics relating to semantic diversity and synonymity, with qualitative observations demonstrating likewise. We also suggest the use of Word Mover's Distance as a semantically intuitive and informative metric for this task.

关键词： Image captioning aesthetic quality assessment text synthesis encoder-decoder network Word Mover's Distance

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DEEP IMAGE DEBLURRING USING LOCAL CORRELATION BLOCK

DEEP IMAGE DEBLURRING USING LOCAL CORRELATION BLOCK

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Su, Wei Yuan, Yuan Wang, Qi Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning Optimal Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781509066315

Dynamic scene deblurring is a challenging problem due to the various blurry source. Many deep learning based approaches try to train end-to-end deblurring networks, and achieve successful performance. However, the architectures and parameters of these methods are unchanged after training, so they need deeper network architectures and more parameters to adapt different blurry images, which increase the computational complexity. In this paper, we propose a local correlation block (LCBlock), which can adjust the weights of features adaptively according to the blurry inputs. And we use it to construct a dynamic scene deblurring network named LCNet. Experimental results show that the proposed LC-Net produces compariable performance with shorter running time and smaller network size, compared to state-of-the-art learning-based methods.

关键词： Dynamic scene deblurring encoder-decoder network Local correlation block

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Detection of black box signal based on encoder-decoder fully convolutional networks

Detection of black box signal based on encoder-decoder fully...

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Global OCEANS Singapore - U.S. Gulf Coast Conference

作者： Ji, Huazhong Zhou, Jie Pan, Xiang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Coll Mat & Environm Engn Hangzhou 310018 Peoples R China

ISBN: (纸本)9781728154466

As a logger of aircraft data, the black box is the most reliable and effective means of identifying the cause of an accident after an aircraft crash. An underwater acoustic beacon was installed in the black box to deal with the black box positioning problem in the air accident at sea. The masking effect of ocean noise, coupled with the propagation loss of the ocean, causes the signal to attenuate seriously during long-distance propagation, which makes it very difficult to detect underwater signals. Inspired by the successful application of fully convolutional networks (FCN) in the field of pixel-level image classification, an encoder-decoder network with skip connnection layers, called "Unet", is proposed to enhance the underwater acoustic beacon signals represented by short-time Fourier transform (STFT) images. The experimental data show that the enhancement method based on FCN has higher signal gain than the conventional method based on adaptive line enhancer (ALE).

关键词： black box signal fully convolutional network (FCN) encoder-decoder network adaptive line enhancer (ALE) siganl enhancement

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TCRNET: MAKE TRANSFORMER, CNN AND RNN COMPLEMENT EACH OTHER 47

TCRNET: MAKE TRANSFORMER, CNN AND RNN COMPLEMENT EACH OTHER

引用

47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Shan, Xinxin Ma, Tai Gu, Anqi Cai, Haibin Wen, Ying East China Normal Univ Sch Commun & Elect Engn Shanghai Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China East China Normal Univ Software Engn Inst Shanghai Peoples R China

ISBN: (纸本)9781665405409

Recently, several Transformer-based methods have been presented to improve image segmentation. However, since Transformer needs regular square images and has difficulty in obtaining local feature information, the performance of image segmentation is seriously affected. In this paper, we propose a novel encoder-decoder network named TCRNet, which makes Transformer, Convolutional neural network (CNN) and Recurrent neural network (RNN) complement each other. In the encoder, we extract and concatenate the feature maps from Transformer and CNN to effectively capture global and local feature information of images. Then in the decoder, we utilize convolutional RNN in the proposed recurrent decoding unit to refine the feature maps from the decoder for finer prediction. Experimental results on three medical datasets demonstrate that TCRNet effectively improves the segmentation precision.

关键词： Transformer-based method feature information encoder-decoder network image segmentation

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REDUCED ORDER MODEL FOR UNSTEADY FLUID FLOWS VIA RECURRENT NEURAL networkS 38

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38th ASME International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2019)

作者： Reddy, Sandeep B. Magee, Allan Ross Jaiman, Rajeev K. Liu, J. Xu, W. Choudhary, A. Hussain, A. A. Natl Univ Singapore Dept Civil & Environm Engn Keppel NUS Corp Lab Singapore Singapore Natl Univ Singapore Dept Mech Engn Singapore Singapore Keppel Offshore & Marine Technol Ctr Keppel Offsh 50 GUL RD Singapore Singapore

ISBN: (纸本)9780791858776

In this paper, we present a data-driven approach to construct a reduced-order model (ROM) for the unsteady flow field and fluid-structure interaction. This proposed approach relies on (i) a projection of the high-dimensional data from the Navier-Stokes equations to a low-dimensional subspace using the proper orthogonal decomposition (POD) and (ii) integration of the lowdimensional model with the recurrent neural networks. For the hybrid ROM formulation, we consider long short term memory networks with encoder-decoder architecture, which is a special variant of recurrent neural networks. The mathematical structure of recurrent neural networks embodies a non-linear state space form of the underlying dynamical behavior. This particular attribute of an RNN makes it suitable for non-linear unsteady flow problems. In the proposed hybrid RNN method, the spatial and temporal features of the unsteady flow system are captured separately. Time-invariant modes obtained by low-order projection embodies the spatial features of the flow field, while the tempo ral behavior of the corresponding modal coefficients is learned via recurrent neural networks. The effectiveness of the proposed method is first demonstrated on a canonical problem of flow past a cylinder at low Reynolds number. With regard to a practical marine/offshore engineering demonstration, we have applied and examined the reliability of the proposed data-driven framework for the predictions of vortex-induced vibrations of a flexible offshore riser at high Reynolds number.

关键词： Data-driven modeling reduced-order model recurrent neural networks proper orthogonal decomposition encoder-decoder network

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encoder-decoder-based image transformation approach for integrating precipitation forecasts 13

Encoder-decoder-based image transformation approach for inte...

引用

13th Asian Conference on Machine Learning (ACML)

作者： Hachiya, Hirotaka Masumoto, Yusuke Mori, Yuki Ueda, Naonori Wakayama Univ Grad Sch Syst Engn Wakayama Japan RIKEN Ctr AIP Wako Saitama Japan Japan Meteorol Agency Numer Predict Dev Ctr Tokyo Japan Fujitsu Ltd Tokyo Japan

As the damage caused by heavy rainfall is becoming more serious, the improvement of precipitation forecasts is highly demanded. For this purpose, arithmetic and Bayesian average-based methods have been proposed to integrate multiple 2D-grid forecasts. However, since a single weight is shared in the entire grid in these methods, local variations of the importance of forecasts could not be taken into account. Besides, although a variety of information is available in precipitation forecast, it would not be straightforwardly to incorporate the additional information in the existing methods. To overcome these problems, we propose an encoder-decoder-based image transformation method that generates a weight image that is optimized in a pixel-wise manner, and additional information could be embedded as the channel of input images and feature maps. Through the experiment of precipitation forecast in the period from April 2018 to March 2019 in Japan, we will show that our proposed integration method outperforms existing methods.

关键词： precipitation forecast image-to-image transformation encoder-decoder network

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