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Interspeech Conference

作者： Singh, Abhayjeet Illa, Aravind Ghosh, Prasanta Kumar Indian Inst Sci IISc Elect Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9781713820697

While speaking at different rates, articulators (like tongue, lips) tend to move differently and the enunciations are also of different durations. In the past, affine transformation and DNN have been used to transform articulatory movements from neutral to fast(N2F) and neutral to slow(N2S) speaking rates [1]. In this work, we improve over the existing transformation techniques by modeling rate specific durations and their transformation using AstNet, an encoder-decoder framework with attention. In the current work, we propose an encoder-decoder architecture using LSTMs which generates smoother predicted articulatory trajectories. For modeling duration variations across speaking rates, we deploy attention network, which eliminates the need to align trajectories in different rates using DTW. We perform a phoneme specific duration analysis to examine how well duration is transformed using the proposed AstNet. As the range of articulatory motions is correlated with speaking rate, we also analyze amplitude of the transformed articulatory movements at different rates compared to their original counterparts, to examine how well the proposed AstNet predicts the extent of articulatory movements in N2F and N2S. We observe that AstNet could model both duration and extent of articulatory movements better than the existing transformation techniques resulting in more accurate transformed articulatory trajectories.

关键词： encoder-decoder Attention LSTM Speaking rate Electromagnetic Articulography

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A method of face repair based on encoder-decoder and dual discrimination network 39

A method of face repair based on encoder-decoder and dual di...

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39th Chinese Control Conference (CCC)

作者： Cui Can Zhao Jun Xiong Xingzhong Edwards, Nathaniel O. Sichuan Univ Sci & Engn Yinbin Peoples R China

ISBN: (纸本)9789881563903

Neural networks have made significant achievements in the field of image restoration. To efficiently repair facial images with large areas damaged, a decoder-encoder structured convolutional neural network is used as a generative model and skip-connection is added between some of its layers to enhance the structure prediction ability of the generated model and well suppressed the problem that the repair network is easy to over-fitting. The global discrimination network mostly uses the image's edge structure and feature information to ensure that the repaired image, which is the output from the repair network, conforms to visual connectivity, while the local discriminators, not only recognize local consistency but also optimize more details. The network structure proposed in this paper combines the encoder-decoder, skip-connection, and dual discriminator networks to improve the effect of face completion. The experimental results on the CelebA show that the proposed method is superior to other methods in repairing images with large areas of damage.

关键词： Generative Adversarial Network (GAN) face inpainting skip-connection encoder-decoder global and local discriminators

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An Odor Labeling Convolutional encoder-decoder for Odor Sensing in Machine Olfaction

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SENSORS 2021年第2期21卷 388-388页

作者： Wen, Tengteng Mo, Zhuofeng Li, Jingshan Liu, Qi Wu, Liming Luo, Dehan Guangdong Univ Technol Dept Electromech Engn Guangzhou Higher Educ Mega Ctr 100 Waihuan Rd W Guangzhou 510006 Peoples R China

Deep learning methods have been widely applied to visual and acoustic technology. In this paper, we propose an odor labeling convolutional encoder-decoder (OLCE) for odor identification in machine olfaction. OLCE composes a convolutional neural network encoder and decoder where the encoder output is constrained to odor labels. An electronic nose was used for the data collection of gas responses followed by a normative experimental procedure. Several evaluation indexes were calculated to evaluate the algorithm effectiveness: accuracy 92.57%, precision 92.29%, recall rate 92.06%, F1-Score 91.96%, and Kappa coefficient 90.76%. We also compared the model with some algorithms used in machine olfaction. The comparison result demonstrated that OLCE had the best performance among these algorithms.

关键词： machine olfactions odor identifications electronic nose neural networks encoder-decoder

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Analysis of Different encoder-decoder-based Approaches for Biomedical Imaging Segmentation 20

Analysis of Different Encoder-decoder-based Approaches for B...

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Proceedings of the 6th International Conference on Robotics and Artificial Intelligence

作者： Rong Zhang Rongguo Zhang Jiechao Ma Huiling Zhang Sun Yat-sen University China Infervision Medical Technology Co.Ltd. China Infervision Medical Technology Co. Ltd. China

ISBN: (纸本)9781450388597

Recently, CNNs (convolutional neural networks) have been widely used in the field of medical image segmentation. In particular, the encoder-decoder architectures represented by U-Net have achieved state-of-art segmentation effects and inspired many more elaborated networks, which adopt newer and more advanced network designs. To our knowledge, the comprehensive and detailed comparison among these improved versions from a multiplicity of points of view has not been conducted up to *** U-Net as the baseline, we select the other four typical improvements for U-Net. For higher reliability, we finish the task of segmentation on four datasets and more experiments are performed to test the performance in various conditions. Finally, we evaluate their performance using multiple evaluation *** find that attention U-Net achieves the best segmentation results in terms of F1-score but also owns the most trainable parameters and is most time-consuming. As training images decrease, the original U-Net is most robust even only less than 5 training samples are available. Besides, for any networks, adding auxiliary loss function with small weighting such as 0.01 or 0.01 whatever the cross-entropy loss and the dice-coefficient loss for the other one is beneficial as well.

关键词： Convolutional neural networks Segmentation encoder-decoder Biomedical image

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A Convolutional encoder-decoder Network With Skip Connections for Saliency Prediction

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IEEE ACCESS 2019年 7卷 60428-60438页

作者： Qi, Fei Lin, Chunhuan Shi, Guangming Li, Hao Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China

In this paper, we propose a novel convolutional encoder-decoder network with skip connections, named CEDNS, to improve the performance of saliency prediction. The encoder network utilizes the DenseNet model as the stem network to extract abundant hierarchical features from input images. Subsequently, a decoder network is designed to sufficiently fuse the hierarchical features to predict saliency more accurately. Between the encoder and decoder, skip connections are employed to transfer hierarchical features produced by the former to the latter. Furthermore, the model can be trained in an end-to-end manner which is beneficial for both training and inference. The experimental results on various benchmark datasets, SALICON, MIT300, and CAT2000, show that the proposed model achieves state-of-the-art performance on several key metrics.

关键词： Saliency convolutional neural networks encoder-decoder skip connections visual attention

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基于encoder-decoder网络的遥感影像道路提取

基于Encoder-Decoder网络的遥感影像道路提取

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作者：李代栋郑州大学

学位级别：硕士

随着遥感技术的飞速发展,高分辨率遥感影像改善了过去较低分辨率影像细节纹理和信息量不充足的问题,对于从遥感影像中提取道路区域的深入研究有极大的促进作用。高分辨率遥感影像中道路区域的提取和应用,对于现代智慧城市设计、智能交... 详细信息

随着遥感技术的飞速发展,高分辨率遥感影像改善了过去较低分辨率影像细节纹理和信息量不充足的问题,对于从遥感影像中提取道路区域的深入研究有极大的促进作用。高分辨率遥感影像中道路区域的提取和应用,对于现代智慧城市设计、智能交通道路网规划和实时路网检测系统等,在现实任务和场景中均具有较为广泛的应用价值和前景。然而在遥感影像中复杂的道路场景,往往导致提取结果中出现断裂、缺失等道路结构不完整,并且存在部分独立散点和斑块等情况。因此,如何利用海量的高分辨率遥感影像,自动、高效地从中提取出路网结构,并精确提取道路区域的边界结构,成为了一个研究的热点领域。本论文主要基于深度学习的相关算法,深入研究了针对高分辨率遥感影像道路区域的高质量提取任务,完成的主要工作如下:(1)针对提取高分辨率遥感影像中道路区域出现结构不完整、道路边界模糊的问题,设计并实现了由提取-优化双网络构成的EDRNet(EDNet)道路提取方法。首先,EDNet1道路提取网络的构建,利用了残差卷积模块保留道路区域信息,同时加速网络收敛,改善道路结构提取不完整的问题;其次,EDNet2道路优化网络,融合多尺度、多层次特征信息,对提取结果中道路边界进行优化改进;最后,根据各网络任务的特点,分别设计相应的损失函数对网络进行训练,充分发挥各网络的性能。(2)针对高分辨率遥感影像中道路区域呈带状分布的显著特征,在影像中狭长的道路区域提取困难的问题,设计并实现了SPUD-Res Net(SPUDNet)道路提取方法。首先,该方法由空洞卷积和残差模块构成编码器部分,并构建跳跃连接和相应的解码器部分,充分保留道路区域的细节信息;其次,在网络中构建条状池化模块和混合池化模块,增强对道路特征信息和道路区域间长距离依赖关系的获取能力;最后,对于网络训练所需的损失函数,利用结构相似性指数与道路的几何结构信息进行针对性设计,能够精细化提取的道路边界,去除网络提取结果之中较为模糊的预测部分。本文对基于encoder-decoder的语义分割网络进行了深入研究,并设计了提取遥感影像中的路网结构的深度学习语义分割方法。本文所提出的算法均能在一定程度上提升道路提取结果的准确性,对未来进行深度语义分割网络的深入研究及相关方法的应用都有十分重要的意义。

关键词：深度学习遥感影像道路提取 encoder-decoder ResNet

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Empirical autopsy of deep video captioning encoder-decoder architecture

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ARRAY 2021年 9卷

作者： Aafaq, Nayyer Akhtar, Naveed Liu, Wei Mian, Ajmal Univ Western Australia 35 Stirling Hwy Perth WA 6009 Australia

Contemporary deep learning based video captioning methods adopt encoder-decoder framework. In encoder, visual features are extracted with 2D/3D Convolutional Neural Networks (CNNs) and a transformed version of those features is passed to the decoder. The decoder uses word embeddings and a language model to map visual features to natural language captions. Due to its composite nature, the encoder-decoder pipeline provides the freedom of multiple choices for each of its components, e.g., the choices of CNN models, feature transformations, word embeddings, and language models etc. Component selection can have drastic effects on the overall video captioning performance. However, current literature is void of any systematic investigation in this regard. This article fills this gap by providing the first thorough empirical analysis of the role that each major component plays in a widely adopted video captioning pipeline. We perform extensive experiments by varying the constituent components of the video captioning framework, and quantify the performance gains that are possible by mere component selection. We use the popular MSVD dataset as the test-bed, and demonstrate that substantial performance gains are possible by careful selection of the constituent components without major changes to the pipeline itself. These results are expected to provide guiding principles for research in the fast growing direction of video captioning.

关键词： CNN architecture encoder-decoder Word embeddings Language model Video captioning Language and vision Video to text Natural language processing Recurrent neural networks

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Deep Hierarchical encoder-decoder Network for Image Captioning

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IEEE TRANSACTIONS ON MULTIMEDIA 2019年第11期21卷 2942-2956页

作者： Xiao, Xinyu Wang, Lingfeng Ding, Kun Xiang, Shiming Pan, Chunhong Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

encoder-decoder models have been widely used in image captioning, and most of them are designed via single long short term memory (LSTM). The capacity of single-layer network, whose encoder and decoder are integrated together, is limited for such a complex task of image captioning. Moreover, how to effectively increase the "vertical depth" of encoder-decoder remains to be solved. To deal with these problems, a novel deep hierarchical encoder-decoder network is proposed for image captioning, where a deep hierarchical structure is explored to separate the functions of encoder and decoder. This model is capable of efficiently exerting the representation capacity of deep networks to fuse high level semantics of vision and language in generating captions. Specifically, visual representations in top levels of abstraction are simultaneously considered, and each of these levels is associated to one LSTM. The bottom-most LSTM is applied as the encoder of textual inputs. The application of the middle layer in encoder-decoder is to enhance the decoding ability of top-most LSTM. Furthermore, depending on the introduction of semantic enhancement module of image feature and distribution combine module of text feature, variants of architectures of our model are constructed to explore the impacts and mutual interactions among the visual representation, textual representations, and the output of the middle LSTM layer. Particularly, the framework is training under a reinforcement learning method to address the exposure bias problem between the training and the testing by the policy gradient optimization. Qualitative analyses indicate the process that our model "translates" image to sentence and further visualization presents the evolution of the hidden states from different hierarchical LSTMs over time. Extensive experiments demonstrate that our model outperforms current state-of-the-art models on three benchmark datasets: Flickr8K, Flickr30K, and MSCOCO. On both image captioning and

关键词： Visualization Semantics Hidden Markov models Decoding Logic gates Training Computer architecture Deep hierarchical structure encoder-decoder LSTM image captioning retrieval vision-sentence

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Automatic Building Extraction on High-Resolution Remote Sensing Imagery Using Deep Convolutional encoder-decoder With Spatial Pyramid Pooling

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IEEE ACCESS 2019年 7卷 128774-128786页

作者： Liu, Yaohui Gross, Lutz Li, Zhiqiang Li, Xiaoli Fan, Xiwei Qi, Wenhua China Earthquake Adm Inst Geol Beijing 100029 Peoples R China Univ Queensland Sch Earth & Environm Sci Brisbane Qld 4072 Australia China Earthquake Networks Ctr Beijing 100045 Peoples R China

Automatic extraction of buildings from remote sensing imagery plays a significant role in many applications, such as urban planning and monitoring changes to land cover. Various building segmentation methods have been proposed for visible remote sensing images, especially state-of-the-art methods based on convolutional neural networks (CNNs). However, high-accuracy building segmentation from high-resolution remote sensing imagery is still a challenging task due to the potentially complex texture of buildings in general and image background. Repeated pooling and striding operations used in CNNs reduce feature resolution causing a loss of detailed information. To address this issue, we propose a light-weight deep learning model integrating spatial pyramid pooling with an encoder-decoder structure. The proposed model takes advantage of a spatial pyramid pooling module to capture and aggregate multi-scale contextual information and of the ability of encoder-decoder networks to restore losses of information. The proposed model is evaluated on two publicly available datasets;the Massachusetts roads and buildings dataset and the INRIA Aerial Image Labeling Dataset. The experimental results on these datasets show qualitative and quantitative improvement against established image segmentation models, including SegNet, FCN, U-Net, Tiramisu, and FRRN. For instance, compared to the standard U-Net, the overall accuracy gain is 1.0% (0.913 vs. 0.904) and 3.6% (0.909 vs. 0.877) with a maximal increase of 3.6% in model-training time on these two datasets. These results demonstrate that the proposed model has the potential to deliver automatic building segmentation from high-resolution remote sensing images at an accuracy that makes it a useful tool for practical application scenarios.

关键词： Deep learning high-resolution remote sensing imagery building extraction fully convolutional networks encoder-decoder

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Learning compact graph representations via an encoder-decoder network

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APPLIED NETWORK SCIENCE 2019年第1期4卷 1页

作者： Lee, John Boaz Kong, Xiangnan Worcester Polytech Inst Worcester MA 01609 USA

Feature representation learning for classification of multiple graphs is a problem with practical applications in many domains. For instance, in chemoinformatics, the learned feature representations of molecular graphs can be used to classify molecules which exhibit anti-cancer properties. In many previous work, including discriminative subgraph mining and graphlet-based approaches, a graph representation is derived by counting the occurrence of various graph sub-structures. However, these representations fail to capture the co-occurrence patterns that are inherently present among different sub-structures. Recently, various methods (e.g., DeepWalk, node2vec) have been proposed to learn representations for nodes in a graph. These methods use node co-occurrence to learn node embeddings. However, these methods fail to capture the co-occurrence relationship between more complex sub-structures in the graph since they were designed primarily for node representation learning. In this work, we study the problem of learning graph representations that can capture the structural and functional similarity of sub-structures (as evidenced by their co-occurrence relationship) in graphs. This is particularly useful when classifying graphs that have very few sub-structures in common. The proposed method uses an encoder-decoder model to predict the random walk sequence along neighboring regions (or sub-structures) in a graph given a random walk along a particular region. The method is unsupervised and can be used to obtain generic feature representations of graphs making it applicable with various types of graphs. We evaluate the learned representations using several real-world datasets on the binary graph classification task. The proposed model is able to achieve superior results against multiple state-of-the-art techniques.

关键词： Deep learning encoder-decoder Graph classification Graph representation RNN

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