Sentiment analysis is one of the most popular research areas in natural language processing.It is extremely useful in many applications,such as social media monitoring and e-commerce.Recent application of deep learnin...Sentiment analysis is one of the most popular research areas in natural language processing.It is extremely useful in many applications,such as social media monitoring and e-commerce.Recent application of deep learning based methods has dramatically changed the research strategies and improved the performance of many traditional sentiment analysis tasks,such as sentiment classification and aspect based sentiment analysis.Moreover,it also pushed the boundary of various sentiment analysis task,including sentiment classification of different text granularities and in different application scenarios,implicit sentiment analysis,multimodal sentiment analysis and generation of sentiment-bearing text.In this paper,we give a brief introduction to the recent advance of the deep learning-based methods in these sentiment analysis tasks,including summarizing the approaches and analyzing the dataset.This survey can be well suited for the researchers studying in this field as well as the researchers entering the field.展开更多
跨模态行人重识别研究的重难点主要来自于行人图像之间巨大的模态差异和模态内差异。针对这些问题,提出一种结合多尺度特征与混淆学习的网络结构。为实现高效的特征提取、缩小模态内差异,将网络设计为多尺度特征互补的形式,分别学习行...跨模态行人重识别研究的重难点主要来自于行人图像之间巨大的模态差异和模态内差异。针对这些问题,提出一种结合多尺度特征与混淆学习的网络结构。为实现高效的特征提取、缩小模态内差异,将网络设计为多尺度特征互补的形式,分别学习行人的局部细化特征与全局粗糙特征,从细粒度和粗粒度两方面来增强网络的特征表达能力。利用混淆学习策略,模糊网络的模态识别反馈,挖掘稳定且有效的模态无关属性应对模态差异,来提高特征对模态变化的鲁棒性。在大规模数据集SYSU-MM01的全搜索模式下该算法首位击中率和平均精度(mean average precision,mAP)的结果分别为76.69%和72.45%,在RegDB数据集的可见光到红外模式下该算法首位击中率和mAP的结果分别为94.62%和94.60%,优于现有的主要方法,验证了所提方法的有效性。展开更多
Near-duplicate image detection is a necessary operation to refine image search results for efficient user exploration. The existences of large amounts of near duplicates require fast and accurate automatic near-duplic...Near-duplicate image detection is a necessary operation to refine image search results for efficient user exploration. The existences of large amounts of near duplicates require fast and accurate automatic near-duplicate detection methods. We have designed a coarse-to-fine near duplicate detection framework to speed-up the process and a multi-modal integra-tion scheme for accurate detection. The duplicate pairs are detected with both global feature (partition based color his-togram) and local feature (CPAM and SIFT Bag-of-Word model). The experiment results on large scale data set proved the effectiveness of the proposed design.展开更多
基金the National Key R&D Program of China(Grant No.2018YFB1005103)the National Natural Science Foundation of China(Grant Nos.61632011 and 61772153)supported by China Scholarship Council(CSC)during a visit to the University of Copenhagen。
文摘Sentiment analysis is one of the most popular research areas in natural language processing.It is extremely useful in many applications,such as social media monitoring and e-commerce.Recent application of deep learning based methods has dramatically changed the research strategies and improved the performance of many traditional sentiment analysis tasks,such as sentiment classification and aspect based sentiment analysis.Moreover,it also pushed the boundary of various sentiment analysis task,including sentiment classification of different text granularities and in different application scenarios,implicit sentiment analysis,multimodal sentiment analysis and generation of sentiment-bearing text.In this paper,we give a brief introduction to the recent advance of the deep learning-based methods in these sentiment analysis tasks,including summarizing the approaches and analyzing the dataset.This survey can be well suited for the researchers studying in this field as well as the researchers entering the field.
文摘跨模态行人重识别研究的重难点主要来自于行人图像之间巨大的模态差异和模态内差异。针对这些问题,提出一种结合多尺度特征与混淆学习的网络结构。为实现高效的特征提取、缩小模态内差异,将网络设计为多尺度特征互补的形式,分别学习行人的局部细化特征与全局粗糙特征,从细粒度和粗粒度两方面来增强网络的特征表达能力。利用混淆学习策略,模糊网络的模态识别反馈,挖掘稳定且有效的模态无关属性应对模态差异,来提高特征对模态变化的鲁棒性。在大规模数据集SYSU-MM01的全搜索模式下该算法首位击中率和平均精度(mean average precision,mAP)的结果分别为76.69%和72.45%,在RegDB数据集的可见光到红外模式下该算法首位击中率和mAP的结果分别为94.62%和94.60%,优于现有的主要方法,验证了所提方法的有效性。
文摘Near-duplicate image detection is a necessary operation to refine image search results for efficient user exploration. The existences of large amounts of near duplicates require fast and accurate automatic near-duplicate detection methods. We have designed a coarse-to-fine near duplicate detection framework to speed-up the process and a multi-modal integra-tion scheme for accurate detection. The duplicate pairs are detected with both global feature (partition based color his-togram) and local feature (CPAM and SIFT Bag-of-Word model). The experiment results on large scale data set proved the effectiveness of the proposed design.
