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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Hoang, Duc-Hieu Tran, Anh-Khoa Minh Dang, Duc Ngoc Tran, Phuong-Nam Dang-Ngoc, Hanh Nguyen, Cuong Tuan Ton Duc Thang University Faculty of Electrical & Electronics Eng. Ho Chi Minh City Viet Nam Ton Duc Thang University Faculty of Electrical and Electronics Ho Chi Minh City Viet Nam Fpt University Dept. of Computing Fundamental Ho Chi Minh City Viet Nam University of Technology Sydney School of Electrical and Data Engineering SydneyNSW Australia Fpt University Dept. of Information Technology Specialization Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350313277

In recent years, the topic of image caption generators has gained significant attention. Several successful projects have emerged in this field, showcasing notable advancements. Image caption generators automatically generate descriptive captions for images through the encoder and decoder mechanisms. The encoder leverages computer vision models, while the decoder utilizes natural language processing models. In this study, we aim to assess a comprehensive set of seven distinct methodologies, including six existing methods from prior research and one newly proposed. These methods are trained and evaluated with bilingual evaluation (BLEU) on the Flickr8K dataset. In our experiments, the proposed ResNet50 - BERT - Bahdanau Attention model outperforms the other models in terms of the BLEU-1 score of 0.532143 and BLEU-4 score of 0.126316. © 2023 IEEE.

关键词： BLEU Deep learning encoder-decoder Flickr8K Image Caption Natural language processing

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An Unsupervised Symmetric Tensor Network for Change Detection in Multitemporal Hyperspectral Images

An Unsupervised Symmetric Tensor Network for Change Detectio...

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Conference on Earth and Space - From Infrared to Terahertz (ESIT)

作者： Liang, Chengfang Chen, Zhao Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9781510661196;9781510661202

Since Hyperspectral images (HSIs) contain a large amount of spectral information, they can provide detailed spectral information and enable accurate CD. However, the spectral heterogeneity of HSIs may lead to false alarms which will reduce detection accuracy. Additionally, it is difficult to collect and annotate pixel-level labels for CD in HSIs. Therefore, we propose an unsupervised symmetric tensor network (USTN) for HSIs CD. We design a novel multidimensional symmetric tensor framework to solve the problem of high-dimensional data processing. Furthermore, the framework integrates a spatial edge loss to preserve detailed spectral-spatial information. Finally, we use feature fusion to suppress the invariant components (i.e., the background) and highlight the variant components (i.e., temporal changes). Experiments on two sets of multitemporal HSIs, Hermiston and Bay Area, demonstrate the effectiveness of USTN for binary change detection.

关键词： Hyperspectral images change detection unsupervised learning symmetric tensor network encoder-decoder

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DSCUNet: A multi-view adaptive fusion network for pavement crack segmentation 7

DSCUNet: A multi-view adaptive fusion network for pavement c...

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7th International Conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Luan, Shen Wang, Chen Zhang, Hongyi Gao, Xingen Mao, Yihai Xiamen Univ Technol Sch Optoelect & Commun Engn Xiamen Peoples R China

ISBN: (纸本)9798400708305

The complexity of cracks in terms of topology, shape, and background makes road crack detection based on computer vision a challenging task. In this study, we consider a network called DSCUNet, which exploits the encoder-decoder structure of U-Net for pavement crack image segmentation. A novel adaptive multi-view feature fusion module is provided in this approach. Inspired by the recently proposed dynamic snake convolutional network (DSCNet), we integrate dynamic snake convolution into the proposed model to enhance its perception of the crack structure during the feature extraction process and to produce a multi-view localized feature map. The adaptive multi-view feature fusion module is used to adaptively fuse multi-view features, aiming to capture local and global features at the same time for accurate positioning. DSCUNet is trained and tested on the CRWKH100 dataset. A detailed comparison of DSCUNet with DSCNet, U-Net, and the existing approaches is provided in our study. The results show that DSCUNet achieves finer results for crack image segmentation, achieving stateof-the-art performance with 0.843 ODS, 0.866 OIS and 0.899 AP on CRWKH100, respectively.

关键词： Crack detection encoder-decoder DSCUNet U-Net Dynamic snake convolution Segmentation

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Improving Long-Term Traffic Prediction with Online Search Log Data 47

Improving Long-Term Traffic Prediction with Online Search Lo...

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47th IEEE-Computer-Society Annual International Conference on Computers, Software, and Applications (COMPSAC)

作者： Matsunaga, Itsuki Kosugi, Yuto Ge Hangli Michikata, Takashi Koshizuka, Noboru Univ Tokyo Grad Sch Interdisciplinary Informat Studies Tokyo Japan Univ Tokyo Interfac Initiat Informat Studies Tokyo Japan

ISBN: (纸本)9798350326970

Long-term traffic prediction is essential for both road managers and users to prepare for future congestion. However, most existing studies have only focused on short-term prediction. Moreover, few studies have effectively incorporated external data into long-term traffic prediction, even though traffic conditions are complexly influenced by various spatiotemporal factors. In this paper, we propose a novel method that utilizes online search log data for long-term traffic prediction on expressways. Online search logs reflect drivers' travel intentions and external factors, such as weather conditions and events, which cannot be represented by historical traffic data. Based on a new analysis of the correlation between online search log data and real-world traffic, we use online search log data as potential future traffic volume in an LSTM-based encoder-decoder model. Experiments using a real-world dataset on an expressway known for frequent congestion show that the use of online search log data improves the metrics of MAE, RMSE, and MAPE in next-day traffic volume prediction by 8.1%, 12.5%, and 7.2% on average, respectively. Similarly, in speed prediction, the MAE, RMSE, and MAPE are reduced by 3.7%, 2.1%, and 11.8%, respectively. It is also shown that online search log data is particularly effective in predicting irregular congestion caused by sudden increases in traffic demand.

关键词： Traffic prediction online search log intelligent transportation system encoder-decoder spatiotemporal data

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An encoder framework for taxi-demand prediction using spatio-temporal function approximation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期126卷

作者： Bhanu, Manish Roy, Saswata Priya, Shalini Mendes-Moreira, Joao Chandra, Joydeep Indian Inst Technol Patna Dept Comp Sci & Engn Dayalpur Daulatpur Bihar India Oak Ridge Natl Lab Oak Ridge TN USA Univ Porto Fac Engn Dept Informat Engn Porto Portugal

Predicting taxi demands in large cities can help in better traffic management as well as ensure better commuter satisfaction for an intelligent transportation system. However, the traffic demands across different locations have varying spatio-temporal correlations that are difficult to model. Despite the ability of the existing Deep Neural Network (DNN) models to capture the non-linearity in spatial and temporal characteristics of the demand time-series, capturing spatio-temporal characteristics in different real-world scenarios like varying historic and prediction time frame, spatio-temporal variations due to noise or missing data, etc. still remain a big challenge for the state-of-the-art models. In this paper, we introduce encoder-ApproXimator (EnAppX), an encoder-decoder DNN-based model that uses Chebyshev function approximation in the decoding stage for taxi demand times-series prediction and can better estimate the time-series in the presence of large spatio-temporal variations. Opposed to any existing state-of-the-art model, the proposed model approximates complete spatiotemporal characteristics in the frequency domain which in turn enables the model to make a robust and improved prediction in different scenarios. Validation over two real-world taxi datasets from different cities shows a considerable improvement of around 23% in RMSE scores compared to the state-of-the-art baseline model. Unlike several existing state-of-the-art models, EnAppX also produces improved prediction accuracy across two regions for both to and fro demands.

关键词： Spatio-temporal encoder-decoder Multi-step prediction Time-series Taxi-demand

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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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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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Enriching Grammatical Error Correction Resources for Modern Greek 13

Enriching Grammatical Error Correction Resources for Modern ...

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13th International Conference on Language Resources and Evaluation (LREC)

作者： Korre, Katerina Pavlopoulos, John Univ Bologna Forli Italy Athens Univ Econ & Business Athens Greece

ISBN: (纸本)9791095546726

Grammatical Error Correction (GEC), a task of Natural Language Processing (NLP), is challenging for under-represented languages. This issue is most prominent in languages other than English. This paper addresses the issue of data and system sparsity for GEC purposes in the modern Greek Language. Following the most popular current approaches in GEC, we develop and test an MT5 multilingual text-to-text transformer for Greek. To our knowledge this the first attempt to create a fully-fledged GEC model for Greek. Our evaluation shows that our system reaches up to 52.63% F0.5 score on part of the Greek Native Corpus (GNC), which is 16% below the winning system of the BEA-19 shared task on English GEC. In addition, we provide an extended version of the Greek Learner Corpus (GLC), on which our model reaches up to 22.76% F0.5. Previous versions did not include corrections with the annotations which hindered the potential development of efficient GEC systems. For that reason we provide a new set of corrections. This new dataset facilitates an exploration of the generalisation abilities and robustness of our system, given that the assessment is conducted on learner data while the training on native data.

关键词： GEC MT5 encoder-decoder

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Crop classification for UAV visible imagery using deep semantic segmentation methods

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GEOCARTO INTERNATIONAL 2022年第25期37卷 10033-10057页

作者： Zhang, Shiqi Dai, Xiaoai Li, Jingzhong Gao, Xiaojie Zhang, Fuxi Gong, Fanxi Lu, Heng Wang, Meilian Ji, Fujiang Wang, Zekun Peng, Peihao Chengdu Univ Technol Coll Earth Sci Chengdu Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan Peoples R China North Carolina State Univ Ctr Geospatial Analyt Raleigh NC USA Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu Peoples R China Sichuan Univ Coll Hydraul & Hydroelect Engn Chengdu Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Auburn Univ Dept Mech Engn Auburn AL 36849 USA Chengdu Univ Technol Tourism & Urban Rural Planning Coll Chengdu Peoples R China

Unmanned aerial vehicle (UAV) has become a mainstream data collection platform in precision agriculture. For more accessible UAV-visible imagery, the high spatial resolution brings the rich geometric texture features triggered large differences in same crop image's features. We proposed an encoder-decoder's fully convolutional neural network combined with a visible band difference vegetation index (VDVI) to perform deep semantic segmentation of crop image features. This model ensures the accuracy and the generalization ability, while reducing parameters and the operation cost. A case study of crop classification was conducted in Chengdu, China, where classified four crops, namely, maize, rice, balsam pear, and Loropetalum chinese, it was shown more effective results. In addition, this study explores a fine crop classification method based on visible light features, which is feasible with low equipment cost, and has a prospect of application in crop survey based on UAV low altitude remote sensing.

关键词： Crop UAV visible images deep semantic segmentation encoder-decoder

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NL-DSE: Non-Local Neural Network with decoder-Squeeze-and-Excitation for Monocular Depth Estimation 48

NL-DSE: Non-Local Neural Network with Decoder-Squeeze-and-Ex...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tsai, Tsung-Han Wan, Wei-Chung National Central University Department of Electrical Engineering Taoyuan City Taiwan

ISBN: (纸本)9781728163277

Monocular Depth Estimation is a popular and challenging problem for many years. IR CNNs (Convolutional Neural Networks)-based method with encoder-decoder architecture is proposed and shows a reasonable result. In this paper, we propose a SE-Net-based module for the decoder part in the encoder-decoder architecture to improve the result. We proposed a DSE (decoder-Squeeze-and-Excitation) module to deal with the whole up-sampling process globally for the decoder part. We also include the Non-local Network space attention method to design the Non-Local decoder-Squeeze-and-Excitation (NL-DSE) module. The proposed NL-DSE module is installed and evaluated on the NYU Depth V2 dataset and achieves higher accuracy. Moreover, the design is independent of the encoder-decoder architecture and can be applied in the other encoder-decoder networks to have a more accurate network. © 2023 IEEE.

关键词： CNNs encoder-decoder monocular depth estimation SE-Net up-sampling recalibration

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