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20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Gupta, Ayush Shukla, Amit Sivarathri, Ashok Kumar Pandya, Naisarg Indian Inst Technol Mandi Sch Mech & Mat Engn Mandi Himachal Prades India Indian Inst Technol Mandi Ctr Artificial Intelligence & Robot Mandi Himachal Prades India

ISBN: (纸本)9798331516246;9798331516239

This research paper focuses on developing vision-based autonomous navigation and motion control algorithms for quadcopters to track high-rise industrial vertical structures. A camera, fixed on the drone will capture the Red-Green-Blue (RGB) channel image and will feed to the image processing node, which is a convolution neural network (CNN) based encoder-decoder model for image segmentation. The segmented image will go to the classical computer vision algorithm to get the desired features for navigation and the motion control algorithm. In this paper, we have implemented a nonlinear sliding mode control algorithm for motion control and discuss the results of positioning and motion control of the drone for the motion in circular and spiral paths. The entire experimentation is performed on the DJI M100 drone with the Nvidia Jetson Xavier NX as an onboard computer.

关键词： Autonomous tracking Computer Vision encoder-decoder and sliding mode control

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Attention-based dual residual network-based for multi-spectral pan-sharpening

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2022年第13期43卷 4911-4950页

作者： Zhang, Xinyu Li, Jinjiang Hua, Zhen Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Coinnovat Ctr Shandong Coll & Univ Future Intelligent Comp Yantai Peoples R China

Pansharpening belongs to an important part in the field of remote sensing image fusion, which refers to the fusion of low spatial resolution multispectral (MS) images with high spatial resolution (PAN) images to finally obtain high resolution multispectral (HRMS) images. The existing deep learning-based pan-sharpening methods have achieved better results compared with the traditional methods, but there are still two problems: spectral distortion and loss of spatial detail information. We propose an end-to-end attention-based dual-residual multi-stage remote sensing image fusion network (ADRPN) for MS image and PAN image pan-sharpening based on an in-depth study of spectral information of MS images and spatial information fusion of PAN images, which consists of three main stages, each resembling an encoder-decoder, and cross-stage fusion to achieve channel domain feature stitching. The first two stages are the feature extraction stage, where the features of MS images and PAN images are extracted using the residual module, and the different features learned are used to guide the training of individual networks using CRFB (Cross residual feature block). In the third stage (image reconstruction), we use the CA (Coordinate attention) and SE (Squeeze-and-Excitation) attention mechanism to enable the network to more precisely locate the region of interest by the precise location information obtained, which allows the features extracted in the first two stages to be better fused with the original image, thus reducing the occurrence of spectral distortion and loss of spatial detail information. Qualitative and quantitative analyses of real and simulated data from the benchmark datasets QuickBird (QB), GF-2, and WorldView-2 (WV2) show that the method can better preserve the spectral and spatial detail information and obtain high-quality HRMS images.

关键词： Pansharpening multispectral (MS) images panchromatic (PAN) images residual block encoder-decoder attention CRFB

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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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A Multiple-Integration encoder for Multi-Turn Text-to-SQL Semantic Parsing

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 1503-1513页

作者： Wang, Run-Ze Ling, Zhen-Hua Zhou, Jing-Bo Hu, Yu Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Hefei 230027 Peoples R China Baidu Res Business Intelligence Lab Beijing 100084 Peoples R China Univ Sci & Technol China Hefei 230027 Peoples R China iFLYTEK Res Hefei 230088 Peoples R China

This paper studies multi-turn text-to-SQL generation, which is a new but important task in semantic parsing. In order to deal with its two challenges, i.e., multi-turn interaction and cross-domain evaluation, this paper proposes a multiple-integration encoder, which derives the vector representations of user utterances and database schemas using three custom-designed modules for information integration. First, an utterance representation enhancing module is built to integrate the information of history utterances into the representation of each token in current utterance by attentive selection. Second, a schema discrepancy enhancing module is designed to integrate previous predicted SQL query into the representation of schema items. Third, a latent schema linking module is employed to integrate schema information into utterance representations for better dealing with unseen database schemas. These three modules are all implemented based on a lightweight multi-head attention mechanism, which reduces the number of parameters in conventional multi-head attention. Experimental results on the SParC dataset show that our method achieved better accuracy of multi-turn text-to-SQL generation than the most advanced benchmarks. Further ablations studies and analysis also demonstrate the effectiveness of the three modules designed for information integration in the encoder.

关键词： Task analysis Structured Query Language Decoding Databases Semantics History Bit error rate Text-to-SQL cross-domain multi-turn encoder-decoder lightweight multi-head attention

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VIG-UNET: VISION GRAPH NEURAL NETWORKS FOR MEDICAL IMAGE SEGMENTATION 20

VIG-UNET: VISION GRAPH NEURAL NETWORKS FOR MEDICAL IMAGE SEG...

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20th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Jiang, Juntao Chen, Xiyu Tian, Guanzhong Liu, Yong Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China Zhejiang Univ Polytech Inst Hangzhou Peoples R China Zhejiang Univ Ningbo Innovat Ctr Ningbo Peoples R China

ISBN: (纸本)9781665473583

Deep neural networks have been widely used in medical image analysis and medical image segmentation is one of the most important tasks. U-shaped neural networks with encoder-decoder are prevailing and have succeeded greatly in various segmentation tasks. While CNNs treat an image as a grid of pixels in Euclidean space and Transformers recognize an image as a sequence of patches, graph-based representation is more generalized and can construct connections for each part of an image. In this paper, we propose a novel ViG-UNet, a graph neural network-based U-shaped architecture with the encoder, the decoder, the bottleneck, and skip connections. The downsampling and upsampling modules are also carefully designed. The experimental results on ISIC 2016, ISIC 2017 and Kvasir-SEG datasets demonstrate that our proposed architecture outperforms most existing classic and state-of-the-art U-shaped networks.

关键词： Medical image segmentation ViG-UNet Graph neural networks encoder-decoder

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Generating Textual Description Using Modified Beam Search 29th

Generating Textual Description Using Modified Beam Search

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29th International Conference on Neural Information Processing

作者： Rai, Divyansh Agarwal, Arpit Kumar, Bagesh Vyas, O. P. Khan, Suhaib Shourya, S. Indian Inst Informat Technol Allahabad Prayagraj India

ISBN: (纸本)9789819916412;9789819916429

Generating textual descriptions of images by describing them in words is a fundamental problem that connects computer vision and natural language processing. A single image may include several entities, their orientations, appearance, and position in a scene as well as their complex spatial interactions, thus leading to a lot of possible captions for an image. Search algorithm of Beam Search has been employed for the task of sentence for the last couple of decades, although it returns around the similar captions with minor changes of wordings. We came across another search strategy, Diverse M-Best which uses M (M denotes the number of independent, diverse beam searches) beam searches from diverse starting statements and keeps the best output from each beam search, and removes the rest of (B-1) captions. This method would mostly lead us to many possible diverse generated sequences, but running Beam Search M several times would be computationally expensive. With the above stated works in vision, we have devised and implemented a novel algorithm, Modified Beam Search (MBS), for generation of Diverse and better captions, with an increase in the computational complexity as compared to the Beam Search. We obtained improvements on BLEU-3 and BLEU-4 scores by 1-3% over the top-2 predicted captions from the original beam search captions.

关键词： CNN encoder-decoder LSTM Modified Beam Search Search Algorithms

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Attention Guided Global Enhancement and Local Refinement Network for Semantic Segmentation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 3211-3223页

作者： Li, Jiangyun Zha, Sen Chen, Chen Ding, Meng Zhang, Tianxiang Yu, Hong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528000 Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Scoop Med Houston TX 77007 USA

The encoder-decoder architecture is widely used as a lightweight semantic segmentation network. However, it struggles with a limited performance compared to a well-designed Dilated-FCN model for two major problems. First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts. Second, low-level features may bring noises to the network decoder through skip connections for the inadequacy of semantic concepts in early encoder layers. To tackle these challenges, a Global Enhancement Method is proposed to aggregate global information from high-level feature maps and adaptively distribute them to different decoder layers, alleviating the shortage of global contexts in the upsampling process. Besides, aLocal Refinement Module is developed by utilizing the decoder features as the semantic guidance to refine the noisy encoder features before the fusion of these two (the decoder features and the encoder features). Then, the two methods are integrated into a Context Fusion Block, and based on that, a novel Attention guided Global enhancement and Local refinement Network (AGLN) is elaborately designed. Extensive experiments on PASCAL Context, ADE20K, and PASCAL VOC 2012 datasets have demonstrated the effectiveness of the proposed approach. In particular, with a vanilla ResNet-101 backbone, AGLN achieves the state-of-the-art result (56.23% mean IOU) on the PASCAL Context dataset. The code is available at https://***/zhasen1996/AGLN.

关键词： Semantics Decoding Image segmentation Interpolation Convolution Aggregates Context modeling Semantic segmentation encoder-decoder global enhancement local refinement context fusion

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ASCNet: 3D object detection from point cloud based on adaptive spatial context features q

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NEUROCOMPUTING 2022年第0期475卷 89-101页

作者： Tong, Guofeng Peng, Hao Shao, Yuyuan Yin, Qijun Li, Zheng Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

This paper presents a novel two-stage 3D point cloud object detector named ASCNet for autonomous driving. Most current works project 3D point clouds to 2D space, whereas the quantization loss in the transformation is inevitable. A Pillar-wise Spatial Context Feature Encoding (PSCFE) module is proposed in the paper to drive the learning of discriminative features and reduce the detailed information loss. The inhomogeneity that existed in 3D object detection from the point clouds, such as the inconsistent number of points in the pillars, the diverse size of Regions of Interest (RoI), should be treated wisely due to the sparsity and the individual specificity. We introduce a length-adaptive RNN-based module to solve the inhomogeneity. A novel backbone combining encoder-decoder and shortcut connection is designed in the paper to learn the multi-scale features for 3D object detection. Additionally, we utilize multiple RoI heads and class-wise NMS to deal with the class imbalance in scenes. Extensive experiments on the KITTI dataset demonstrate that our algorithm achieves competitive performance in 3D bounding box detection and BEV detection. (c) 2021 Elsevier B.V. All rights reserved.

关键词： 3D object detection Point clouds Pillar-wise Inhomogeneity encoder-decoder

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Symbolic Integration by Integrating Learning Models With Different Strengths and Weaknesses

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IEEE ACCESS 2022年 10卷 47000-47010页

作者： Kubota, Hazumi Tokuoka, Yuta Yamada, Takahiro G. Funahashi, Akira Keio Univ Grad Sch Fundamental Sci & Technol Ctr Biosci & Informat Yokohama Kanagawa 2238522 Japan Keio Univ Dept Biosci & Informat Yokohama Kanagawa 2238522 Japan

Integration is indispensable, not only in mathematics, but also in a wide range of other fields. A deep learning method has recently been developed and shown to be capable of integrating mathematical functions that could not previously be integrated on a computer. However, that method treats integration as equivalent to natural language translation and does not reflect mathematical information. In this study, we adjusted the learning model to take mathematical information into account and developed a wide range of learning models that learn the order of numerical operations more robustly. In this way, we achieved a 98.80% correct answer rate with symbolic integration, a higher rate than that of any existing method. We judged the correctness of the integration based on whether the derivative of the primitive function was consistent with the integrand. By building an integrated model based on this strategy, we achieved a 99.79% rate of correct answers with symbolic integration. In summary, we have developed a more accurate method of selecting the correct model than the existing method by judging the result of symbolic integration based on whether the output of the model equals the input formula when the output is differentiated.

关键词： Mathematical models Earth Observing System Transformers Solid modeling Deep learning Computational modeling Syntactics Deep learning encoder-decoder supervised learning symbolic integration translation model

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Hand gesture segmentation against complex background based on improved atrous spatial pyramid pooling

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第9期14卷 11795-11807页

作者： Cui, Zhenchao Lei, Yu Wang, Yuxiao Yang, Wenzhu Qi, Jing Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Hebei Peoples R China Hebei Univ Hebei Machine Vis Engn Res Ctr Baoding 071002 Hebei Peoples R China

Gesture segmentation is an essential part of gesture detection. The accuracy of gesture detection can be improved by using gesture segmentation to remove the background part un-hand images. However, the inaccurate features of current methods can greatly affect the accuracy of results in segmentation and gesture recognition. In order to solve this problem and obtain accurate features, this paper proposes the improved atrous spatial pyramid pooling (IASPP). IASPP is a pooling layer in convolution neural network, which can refine features by connecting cascade model and parallel model in atrous spatial pyramid pooling. Otherwise, in order to improve the segmentation performance by integrating details and spatial location information at different levels, the IASPP is embedded in the encoder-decoder, and we name the method the improved atrous spatial pyramid pooling-ResNet (IASPP-ResNet) for gesture segmentation. In the experiment part of this paper, we test the proposed method by comparing it with the states of art on the two datasets of OUTHANDS and HGR. It can be seen that IASPP-ResNet can achieve 97.75% Pixel Accuracy and 89.60% MIoU on the OUTHANDS dataset. The Pixel Accuracy and MIoU of the presented method on the HGR dataset can reach 99.09% and 97.52%, respectively. These presented that our method is superior to the states of art.

关键词： Hand gesture segmentation Complex background encoder-decoder Atrous convolution

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