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Persona aware Response Generation with Emotions

Persona aware Response Generation with Emotions

International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Firdaus, Mauajama Thangavelu, Naveen Ekbal, Asif Bhattacharyya, Pushpak Indian Inst Technol Patna Dept Comp Sci & Engn Patna 801103 Bihar India

ISBN: (纸本)9781728169262

Conversational systems are the perfect examples of human-machine interactions. The conversational agents while interacting with humans lack the ability to express emotions and behave inconsistently, making the conversations boring and non-interactive. In this work, we propose the task of persona aware emotional response generation in which the system can generate specific and consistent responses in accordance to the provided personality information and the conversational history. To make the responses interactive and interesting we intend to infuse the emotions in the responses that help in making the responses more human-like. We propose a persona aware attention framework employing an encoder-decoder approach. We investigate different ways to include the desired emotions in the responses. Experimental results on the PersonaChat dataset shows that our proposed framework outperforms the baseline models and can generate interactive and emotional responses.

关键词： Response generation Persona Emotions Attention encoder-decoder

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HYBRID AUTOREGRESSIVE TRANSDUCER (HAT)

HYBRID AUTOREGRESSIVE TRANSDUCER (HAT)

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

作者： Variani, Ehsan Rybach, David Allauzen, Cyril Riley, Michael

ISBN: (纸本)9781509066315

This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoder-decoder model that preserves the modularity of conventional automatic speech recognition systems. The HAT model provides a way to measure the quality of the internal language model that can be used to decide whether inference with an external language model is beneficial or not. We evaluate our proposed model on a large-scale voice search task. Our experiments show significant improvements in WER compared to the state-of-the-art approaches (1).

关键词： ASR encoder-decoder Beam Search

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Polarization image fusion with self-learned fusion strategy

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PATTERN RECOGNITION 2021年 118卷 108045-108045页

作者： Zhang, Junchao Shao, Jianbo Chen, Jianlai Yang, Degui Liang, Buge Cent South Univ Sch Aeronaut & Astronaut Changsha 410083 Peoples R China

Polarization image fusion aims to integrate intensity and degree of linear polarization images into one with more details, which is beneficial to improve the ability of targets detection under complex background. The fusion strategies in conventional methods are designed in a hand-crafted way and not robust to different fusion tasks. In this paper, we propose a novel and deep network to address the polarization image fusion issue with self-learned strategy. The network consists of encoder, Fusion, and decoder layers. Feature maps extracted by encoder are fused, then fed into decoder to generate fused images. Besides, a novel loss function is adopted to train the network in an unsupervised way, without ground truth of fused images. To verify the advantage, the network trained on polarization images is also used to infrared and visible images fusion, and multi-focus image fusion. Experimental results showed that our method outperforms several state-of-the-art methods in terms of visual quality and quantitative measurement. The proposed fused method can be applied in the military and civilian fields such as camouflage and hidden targets detection, medical diagnosis, and environmental monitoring. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Image fusion encoder-decoder Polarization image Unsupervised learning

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Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning

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SENSORS 2021年第9期21卷 3152-3152页

作者： Liang, Peng Shi, Wenzhong Ding, Yixing Liu, Zhiqiang Shang, Haolv Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Piesat Informat Technol Co Ltd Beijing 100195 Peoples R China

Accurate and up-to-date road network information is very important for the Geographic Information System (GIS) database, traffic management and planning, automatic vehicle navigation, emergency response and urban pollution sources investigation. In this paper, we use vector field learning to extract roads from high resolution remote sensing imaging. This method is usually used for skeleton extraction in nature image, but seldom used in road extraction. In order to improve the accuracy of road extraction, three vector fields are constructed and combined respectively with the normal road mask learning by a two-task network. The results show that all the vector fields are able to significantly improve the accuracy of road extraction, no matter the field is constructed in the road area or completely outside the road. The highest F1 score is 0.7618, increased by 0.053 compared with using only mask learning.

关键词： road extraction vector field learning high resolution remote sensing image encoder-decoder DCNN

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Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks

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GEOPHYSICS 2021年第2期86卷 V131-V142页

作者： Liu, Qun Fu, Lihua Zhang, Meng China Univ Geosci Wuhan Sch Math & Phys Wuhan 430074 Peoples R China Cent China Normal Univ Dept Comp Sci Wuhan 430079 Peoples R China

The reconstruction of seismic data with missing traces has been a long-standing issue in seismic data processing. Deep learning (DL) has emerged as a popular tool for seismic interpolation;it learns priors from training data sets of incomplete/complete data pairs. However, these DL methods are restricted to training data because they are supervised. When the features of the testing and training data are different, the recovery performance decreases, which prevents practical application. We have introduced a "deep-seismic-prior-based" approach via a convolution neural network (CNN), which captures priors based on the particular structure of the CNN, but it does not need any training data set. The ill-posed inverse problem in seismic interpolation is thus solved using the CNN structure as a prior, and the learned network weights are the parameters that represent the seismic data. Because the convolutional filter weights are shared to achieve spatial invariance, the CNN structure can function as a regularizer to guide network learning. In our method, corrupted seismic data are reconstructed during the iterative process by minimizing the mean square error between the network output and the original data. We applied our method for interpolating irregularly and regularlymissing traces in prestack and poststack seismic data. The experimental results indicate that our approach outperforms the traditional singular spectrum analysis and the dealiased Cadzow methods commonly used in the reconstruction of such data.

关键词： convolutional neural networks deep seismic prior encoder-decoder seismic data reconstruction

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Attention-guided image captioning with adaptive global and local feature fusion

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2021年 78卷 103138-103138页

作者： Zhong, Xian Nie, Guozhang Huang, Wenxin Liu, Wenxuan Ma, Bo Lin, Chia-Wen Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Univ Sch Comp Sci & Informat Engn Wuhan Hubei Peoples R China Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan Natl Tsing Hua Univ Inst Commun Engn Hsinchu Taiwan

Although attention mechanisms are exploited widely in encoder-decoder neural network-based image captioning framework, the relation between the selection of salient image regions and the supervision of spatial information on local and global representation learning was overlooked, thereby degrading captioning performance. Consequently, we propose an image captioning scheme based on adaptive spatial information attention (ASIA), extracting a sequence of spatial information of salient objects in a local image region or an entire image. Specifically, in the encoding stage, we extract the object-level visual features of salient objects and their spatial bounding-box. We obtain the global feature maps of an entire image, which are fused with local features and the fused features are fed into the LSTM-based language decoder. In the decoding stage, our adaptive attention mechanism dynamically selects the corresponding image regions specified by an image description. Extensive experiments conducted on two datasets demonstrate the effectiveness of the proposed method.

关键词： Image captioning encoder-decoder Spatial information Adaptive attention

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DFBDehazeNet: an end-to-end dense feedback network for single image dehazing

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JOURNAL OF ELECTRONIC IMAGING 2021年第3期30卷 033004-033004页

作者： Guo, Mengyan Huang, Bo Zhang, Juan Wang, Feng Zhang, Yan Fang, Zhijun Shanghai Univ Engn Sci Sch Elect & Elect Engn Control Engn Shanghai Peoples R China

The feedback mechanism method of simulating the biological vision system has not been widely used in deep learning dehazing algorithms. To alleviate the difficulty of feature interaction, we combine the feedback mechanism with dense skip connections to fuse features of different levels in a dehazing network. Inspired by the feedback network in which previous network layers can have access to rich information processed by the following network layers, we propose an end-to-end dense feedback network (DFBDehazeNet) for single image dehazing that implements the feedback mechanism using hidden states of constrained RNN. The low-level hazy feature information can be continuously corrected by the high-level feature information obtained from the dense feedback block via the recurrent feedback connection. The top-down feedback mechanism is adopted in DFBDehazeNet to refine the low-level hazy feature information, thereby achieving a powerful image restoration effect. The ablation experiment proves that the iterative structure of DFBDehazeNet and the projection unit play an important role in removing haze from images. The experimental results show that the results of image haze removal are superior to the great majority of existing methods both qualitatively and quantitatively. (c) 2021 SPIE and IS&T [DOI: 10.1117/***.30.3.033004] Under the influence of severe weather, the quality of images collected by the camera system drops sharply. These degraded images not only affect people's subjective judgment but also lead to poor results on advanced computer vision tasks such as object detection,1 semantic segmentation,2 action recognition,3 and so on. The restoration of images in severe weather4 is of great significance to computer vision. Image dehazing algorithms are designed to recover high-quality clear images from low-quality hazy images for advanced computer vision tasks. The image dehazing task assists in the driving technology of unmanned vehicles and the unmanned toll sys

关键词： image dehazing dense feedback recurrent iteration encoder-decoder

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Modeling social interaction and intention for pedestrian trajectory prediction

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2021年 570卷 125790-125790页

作者： Chen, Kai Song, Xiao Ren, Xiaoxiang Beihang Univ BUAA Beijing Peoples R China Nanan Primary Sch Nanan Shanxi Peoples R China

Future pedestrian trajectory prediction offers great prospects for many practical applications. Most existing methods focus on social interaction among pedestrians but ignore the fact that in addition to pedestrians there are other kinds of objects (cars, dogs, bicycles, motorcycles, etc.) with a great influence on the subject pedestrian's future trajectory. Most existing methods neglect the intentions of the pedestrian, which can be obtained by the key points of the subject pedestrian's face. Therefore, rich category information about the subject pedestrian's surroundings and face key points plays a great role in promoting the modeling of pedestrian movement. Motivated by this idea, this paper tries to predict a pedestrian's future trajectory by jointly using various categories and the relative positions of the subject pedestrian's surroundings and the key points in his face. We propose a data modeling method to effectively unify rich visual features about categories, interaction and face key points into a multi-channel tensor and build an end-to-end fully convolutional encoder-decoder attention model based on convolutional long-short-term memory utilizing this tensor. We evaluate and compare our method with several existing methods on 5 crowded video sequences from the public dataset multi-object tracking (MOT)-16. Experimental results show that our method outperforms state-of-the-art approaches, with less prediction error. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Social-interaction Pedestrian intention Convolutional long-short-term memory encoder-decoder Attention

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基于Attention模型的法律文书生成研究

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无线互联科技 2023年第1期20卷 111-115,129页

作者：徐惠苏同俞鹏飞江全胜朱咸军南京航空航天大学江苏南京210016 金陵科技学院江苏南京211169 东部战区总医院江苏南京210002 江苏省信息分析工程研究中心江苏南京211169

法律文书的自动生成可以有效缓解法律服务行业中人力资源不足的问题,让用户足不出户就可方便享受到法律咨询服务。适用于法律文书的自动生成技术的研究,在减轻法律工作者文书工作上和普通人叙述法律案件时更规范地描述法律内容具有重要... 详细信息

法律文书的自动生成可以有效缓解法律服务行业中人力资源不足的问题,让用户足不出户就可方便享受到法律咨询服务。适用于法律文书的自动生成技术的研究,在减轻法律工作者文书工作上和普通人叙述法律案件时更规范地描述法律内容具有重要的现实意义。文章提出一种筛选案件要素信息,在encoder-decoder模型中加入注意力机制的Attention模型,最终生成合格的法律文书。实验表明,该模型优化了LSTM模型对长文本的记忆效果,能够较好地完成生成法律文书任务。

关键词：法律文书 LSTM encoder-decoder Attention模型

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Evolution of machine learning in environmental science-A perspective

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ENVIRONMENTAL DATA SCIENCE 2022年 1卷 e3-e3页

作者： Hsieh, William W. Univ British Columbia Dept Earth Ocean & Atmospher Sci Vancouver BC V6T IZ4 Canada 4028 Hopesmore Dr Victoria BC V8N 5S9 Canada

The growth of machine learning (ML) in environmental science can be divided into a slow phase lasting till the mid-2010s and a fast phase thereafter. The rapid transition was brought about by the emergence of powerful new ML methods, allowing ML to successfully tackle many problems where numerical models and statistical models have been hampered. Deep convolutional neural network models greatly advanced the use of ML on 2D or 3D data. Transfer learning has allowed ML to progress in climate science, where data records are generally short for ML. ML and physics are also merging in new areas, for example: (a) using ML for general circulation model parametrization, (b) adding physics constraints in ML models, and (c) using ML in data assimilation. Impact Statement This perspective paper reviews the evolution and growth of machine learning (ML) models in environmental science. The opaque nature of ML models led to decades of slow growth, but exponential growth commenced around the mid-2010s. Novel ML models which have contributed to this exponential growth (e.g., deep convolutional neural networks, encoder-decoder networks, and generative-adversarial networks) are reviewed, as well as approaches to merging ML models with physics-based models.

关键词： Data assimilation encoder-decoder generative-adversarial network machine learning neural network

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