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检索条件"主题词=encoder-decoder"

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ATTENTIVE DUAL STREAM SIAMESE U-NET FOR FLOOD DETECTION ON MULTI-TEMPORAL SENTINEL-1 DATA

ATTENTIVE DUAL STREAM SIAMESE U-NET FOR FLOOD DETECTION ON M...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Yadav, Ritu Nascetti, Andrea Ban, Yifang KTH Royal Inst Technol Div Geoinformat Stockholm Sweden

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly available Sen1Flood11 [1] benchmark dataset. The network outperformed the existing state-of-the-art (uni-temporal) flood detection method by 6% IOU. The experiments highlight that the combination of bi-temporal SAR data with an effective network architecture achieves more accurate flood detection than uni-temporal methods.

关键词： Flood Detection bi-temporal Change Detection SAR Siamese Deep Learning encoder-decoder Attention

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Unsupervised Code-switched Text Generation from Parallel Text 24

Unsupervised Code-switched Text Generation from Parallel Tex...

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Interspeech Conference

作者： Chi, Jie Lu, Brian Eisner, Jason Bell, Peter Jyothi, Preethi Ali, Ahmed M. Univ Edinburgh Ctr Speech Technol Res Edinburgh Midlothian Scotland Johns Hopkins Univ Ctr Language & Speech Proc Baltimore MD USA Indian Inst Technol Dept Comp Sci Bombay Maharashtra India HBKU Qatar Comp Res Inst Ar Rayyan Qatar

There has been great interest in developing automatic speech recognition (ASR) systems that can handle code-switched (CS) speech to meet the needs of a growing bilingual population. However, existing datasets are limited in size. It is expensive and difficult to collect real transcribed spoken CS data due to the challenges of finding and identifying CS data in the wild. As a result, many attempts have been made to generate synthetic CS data. Existing methods either require the existence of CS data during training, or are driven by linguistic knowledge. We introduce a novel approach of forcing a multilingual MT system that was trained on non-CS data to generate CS translations. Comparing against two prior methods, we show that simply leveraging the shared representations of two languages (Mandarin and English) yields better CS text generation and, ultimately, better CS ASR.

关键词： code-switching text generation data augmentation encoder-decoder unsupervised learning

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EVALUATION OF SEVERAL FULLY CONVOLUTIONAL NETWORKS IN SAR IMAGE CHANGE DETECTION 14

EVALUATION OF SEVERAL FULLY CONVOLUTIONAL NETWORKS IN SAR IM...

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ISPRS TC III and TC IV 14th GeoInformation for Disaster Management (Gi4DM)

作者： Ji, Linxia Zhao, Zheng Huo, Wenhao Zhao, Jinqi Gao, Rui Chinese Acad Surveying & Mapping Beijing 100830 Peoples R China Shandong Univ Sci & Technol Qingdao 266590 Peoples R China China Univ Min & Technol Xuzhou 221116 Jiangsu Peoples R China

In recent years, the world is suffering from frequent natural disasters. Change detection (CD) technology can quickly identify the change information on the ground and has developed into an important means of disaster monitoring and assessment. Synthetic aperture radar (SAR) has the characteristics of periodic observation and wide coverage. Moreover, SAR has the advantages of penetrating, all-day and all-weather observation, which plays an important role in disaster monitoring. Due to the rapid development of satellite sensors, the available CD data has been greatly enriched. This situation provides an opportunity for deep learning change detection (DLCD) techniques. However, SAR data are affected by speckle noise and lack of available labeled samples, it remains challenging to precisely locate the change information with high efficiency. This paper focuses on several commonly used and outstanding networks in the DLCD field to evaluate their performance and develop them to SAR data. In addition, Transfer learning experiments are designed to evaluate the generalization performance of each network for the CD task. The experimental results show that the Siamese CD network encoding multi-temporal data separately has the best ability to detect changes and generalization performance. In addition, adding high quality explicit difference guidance information to the network is more specific for the CD task, which can further improve network performance and refine the boundaries of changed ground objects on change map.

关键词： Change Detection SAR Siamese network encoder-decoder Transfer learning Generalization

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A Lightweight and Dynamic Convolutional Network for Real-time Semantic Segmentation 35

A Lightweight and Dynamic Convolutional Network for Real-tim...

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35th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China Shenyang Siasun Robot & Automat Co Ltd Shenyang Peoples R China

ISBN: (纸本)9798350334722

Semantic segmentation is a difficult task that satisfies most of the demands of autonomous driving and drone aerial photography in a unified fashion. Convolutional neural networks can properly categorize image pixels via end-to-end model training. However, achieving the optimal trade-off between segmentation precision and the number of network parameters while maintaining a suitable inference time has become a challenging task. In this paper, we propose a lightweight dynamic convolutional semantic segmentation network, LDCNet, which belongs to the asymmetric network architecture. First, we designed a coding module that includes dynamic convolution: DDAB. The success of this module is attributed to the use of dynamic convolution, which increases the utilization of local and contextual information of the features. We also designed the decoding module containing feature pyramids and hybrid attention: HA-FP,which performs a multi-scale fusion of features accompanied by feature selection. On the Cityscapes and Camvid datasets, LDCNet obtains 73.5 mIoU and 69.4 mIoU accuracy with 78.4 FPS and 91.3 FPS, respectively, without pre-training or post-processing. Our experimental findings reveal that LDCNet achieves an outstanding balance between segmentation accuracy and network parameters with just 0.96 M.

关键词： Lightweight network Dynamic convolution encoder-decoder Semantic segmentation Attention mechanism

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DIALOG STATE TRACKING WITH ATTENTION-BASED SEQUENCE-TO-SEQUENCE LEARNING

DIALOG STATE TRACKING WITH ATTENTION-BASED SEQUENCE-TO-SEQUE...

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IEEE Workshop on Spoken Language Technology (SLT)

作者： Hori, Takaaki Wang, Hai Hori, Chiori Watanabe, Shinji Harsham, Bret Le Roux, Jonathan Hershey, John R. Koji, Yusuke Jing, Yi Zhu, Zhaocheng Aikawa, Takeyuki Mitsubishi Elect Res Labs Cambridge MA 02139 USA Toyota Technol Inst Chicago IL USA Mitsubishi Electr Corp Informat Technol R&D Ctr Tokyo Tokyo Japan Peking Univ Beijing Peoples R China

ISBN: (纸本)9781509049035

We present an advanced dialog state tracking system designed for the 5th Dialog State Tracking Challenge (DSTC5). The main task of DSTC5 is to track the dialog state in a human-human dialog. For each utterance, the tracker emits a frame of slot-value pairs considering the full history of the dialog up to the current turn. Our system includes an encoder-decoder architecture with an attention mechanism to map an input word sequence to a set of semantic labels, i.e., slot-value pairs. This handles the problem of the unknown alignment between the utterances and the labels. By combining the attention-based tracker with rule-based trackers elaborated for English and Chinese, the F-score for the development set improved from 0.475 to 0.507 compared to the rule-only trackers. Moreover, we achieved 0.517 F-score by refining the combination strategy based on the topic and slot level performance of each tracker. In this paper, we also validate the efficacy of each technique and report the test set results submitted to the challenge.

关键词： Dialog state tracking attention model sequence-to-sequence learning encoder-decoder long short-term memory

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Universal adversarial perturbation for remote sensing images 24

Universal adversarial perturbation for remote sensing images

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IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

作者： Wang, Qingyu Feng, Guorui Yin, Zhaoxia Luo, Bin Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Shanghai Univ Sch Commun & Elect Engn Shanghai Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai Peoples R China

ISBN: (数字)9781665471893

ISBN: (纸本)9781665471893

Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditional technology. However, even the state-of-the-art object recognition convolutional neural networks are fooled by the universal adversarial perturbation (UAP). The research on UAP is mostly limited to ordinary images, and RSIs have not been studied. To explore the basic characteristics of UAPs of RSIs, this paper proposes a novel method combining an encoder-decoder network with an attention mechanism to generate the UAP of RSIs. Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model. Finally, the generated regions are used to fine-tune the perturbation making the model misclassified with fewer perturbations. The experimental results show that the UAP can make the classification model misclassify, and the attack success rate of our proposed method on the RSI data set is as high as 97.09%.

关键词： Remote sensing image deep learning universal adversarial perturbation encoder-decoder attention mechanism

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Radar Echo Image Prediction Algorithm Based on Multi-scale Encoding-Decoding Network 22nd

Radar Echo Image Prediction Algorithm Based on Multi-scale E...

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22nd International Conference on Intelligent Data Engineering and Automated Learning

作者： Mou, Xingang Liu, Chang Zhou, Xiao Wuhan Univ Technol Mech & Elect Engn Wuhan Peoples R China

ISBN: (纸本)9783030916077;9783030916084

The traditional technology of radar echo image extrapolation for rainfall nowcasting faces such problems as insufficiently high accuracy, the incomplete analysis of the data on radar echo images, and the image blurring from the stacked LSTM(Long Short-Term Memory). In order to more accurately and clearly predict the radar echo image at a future moment, an adversarial prediction network based on multi-scale U-shaped encoder-decoder is proposed. To overcome the problem of insufficient details of the predicted image, the generator of the network adopts a U-shaped encoder-decoder structure with jump-layer connection. At the same time, in order to capture the echo movement at different scales, multi-scale convolution kernels is introduced to the encoder-decoder units. Then the conventional discriminator structure is improved and stacked ConvLSTM(Convolutional Long Short-Term Memory) layers were proposed to classify sequence. Based on the prediction of next ten frames from the given ten frames of images, this paper tests the network on the SRAD(Standardized Radar Dataset), and compares the prediction results of different networks. The test results show that the proposed model reduces image blurring, enhances the prediction accuracy while retaining sufficient prediction details.

关键词： encoder-decoder Multi-scale Prediction Rainfall nowcasting

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CIFT: Connected Intelligent Fund Transaction System Based on Deep Learning

CIFT: Connected Intelligent Fund Transaction System Based on...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Hu, Gang Ye, Yi Zhang, Yin Hossain, M. Shamim Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China King Saud Univ Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9781728109602

Fund correlation analysis can guide investors' investment and wealth management, avoiding the selection of highly relevant funds in the investment process, which can make the risk sharing among funds. There is a strong dependence between the features of the fund data and a long-term dependence between the output of different time steps, which makes it difficult to obtain good performance in the fund data in the data analysis model used in the traditional intelligent investment system. This has brought difficulties to fund correlation analysis. However, some studies in recent years have shown that the LSTM (Long Short-Term Memory) model has good time series processing capability, and the encoder-decoder model has made great progress in the application of financial time series analysis. Based on the above research, this paper constructs the DLIFT system using an Improved RNN model combined with attention mechanism.. The attention mechanism can select specific feature inputs and previous time step outputs, both of which are highly correlated with the current output, making system predictions more efficient. This paper applies this model to the historical data set containing multiple public funds, and compares it with several other intelligent investment systems. The results show that the fund intelligent investment system proposed in this paper performs best. The research in this paper is of great significance to the use of deep learning methods to solve fund correlation analysis problems, and provides new ideas for the research in the field of smart finance.

关键词： Fund intelligent transaction system Deep learning encoder-decoder Attention mechanism

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AAT: An Efficient Model for Synthesizing Long Sequences on a Small Dataset 13th

AAT: An Efficient Model for Synthesizing Long Sequences on a...

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13th Multi-Disciplinary International Conference on Multi-Disciplinary Trends in Artificial Intelligence (MIWAI)

作者： Quan Anh Minh Nguyen Quang Trinh Le Huu Quoc Van Duc Dung Nguyen HO CHI MINH City Univ Technol Ho Chi Minh City Vietnam

ISBN: (纸本)9783030337094;9783030337087

This work represents an alternative model for speech synthesis, which addresses some major disadvantages of current end-to-end models. Current state-of-the-art models still have some troubles while dealing with long sentences and the size of the dataset. Our proposed Adaptive Alignment Tacotron (AAT) model, however, has achieved impressive results for a very small dataset of the Vietnamese language. By leveraging the nature of diagonal alignment between phoneme and acoustic sequences, we address the issue with long sequences and the proposed model can also be trained efficiently on the small dataset (5 h of recording). The proposed model consists of the following components: a stacked convolutional encoder, a local diagonal attention module, a decoder with schedule teacher forcing to produce a coarse mel-spectrogram prediction, and a converter to transform the melspectrogram to linear-spectrogram. Experimental results show that the proposed model achieves faster convergence speed and higher stability than the baseline model and open a feasible approach for speech synthesis on languages with small dataset.

关键词： Text-to-speech Attention encoder-decoder Sequence-to-sequence Tacotron Vietnamese speech synthesis

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Global Stock Market Prediction Using Transformer-Based Deep Learning Techniques 15

Global Stock Market Prediction Using Transformer-Based Deep ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gothai, E. Thamilselvan, R. Natesan, P. Vignesh, K. Kongu Engineering College Department of Computer Technology Tamilnadu Perundurai India Kongu Engineering College Department of Cse Tamilnadu Perundurai India Kongu Engineering College Department of It Tamilnadu Perundurai India Kongu Engineering College Software Systems Department of Ct Tamilnadu Perundurai India

ISBN: (纸本)9798350370249

Deep learning approaches' use in financial market forecasting has recently drawn a lot of attention from both investors and scholars. The Transformer framework, initially created for natural language processing, is used in this study to propose a revolutionary approach for forecasting the stock market indices of seven different economies: China, Canada, India, Japan, Russia, the United Kingdom, and the United States. The well-known Transformer architecture, which excels at capturing complex non-linear patterns, is modified to study the unique dynamics of each nation's stock market. The model successfully determines the basic principles driving market behavior by using an encoder-decoder structure and a multi-head attention mechanism. The aforementioned indices are used in back-testing trials, which cover a variety of economic environments. The outcomes demonstrate that the Transformer performs better than conventional techniques, with Mean Squared Error (MSE) acting as a crucial evaluation criterion. Notably, the model shows promise for investors in these widely separated markets to receive excess profits. This study offers a thorough analysis of stock market forecasting, expanding its relevance to a wider global context. comprehension of how this strategy might adjust to various financial ecosystems is improved by the addition of seven unique economies. Future research may go more deeply into applications that are industry-specific and investigate potential extensions to additional international markets. © 2024 IEEE.

关键词： Deep Learning encoder-decoder Stock Prediction Transformer

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