在机器学习中,表征学习或表示学习是允许系统从原始数据中自动发现特征检测或分类所需的表示的一组技术。这取代了手动特征工程,并允许机器学习特征并使用它们执行特定任务。在有监督的表征学习中,使用标记的输入数据来学习特征,包括监督神经网络,多层感知器和(监督)字典学习。在无监督表征学习中,特征是与未标记的输入数据一起学习的,包括字典学习,独立成分分析,自动编码器,矩阵分解和各种形式的聚类。

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我们生活在一个由大量不同模态内容构建而成的多媒体世界中,不同模态信息之间具有高度的相关性和互补性,多模态表征学习的主要目的就是挖掘出不同模态之间的共性和特性,产生出可以表示多模态信息的隐含向量.该文章主要介绍了目前应用较广的视觉语言表征的相应研究工作,包括传统的基于相似性模型的研究方法和目前主流的基于语言模型的预训练的方法.目前比较好的思路和解决方案是将视觉特征语义化然后与文本特征通过一个强大的特征抽取器产生出表征,其中Transformer[1]作为主要的特征抽取器被应用表征学习的各类任务中.文章分别从研究背景、不同研究方法的划分、测评方法、未来发展趋势等几个不同角度进行阐述.

http://www.jos.org.cn/jos/ch/reader/view_abstract.aspx?file_no=6125&flag=1

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We present PSEUDo, an adaptive feature learning technique for exploring visual patterns in multi-track sequential data. Our approach is designed with the primary focus to overcome the uneconomic retraining requirements and inflexible representation learning in current deep learning-based systems. Multi-track time series data are generated on an unprecedented scale due to increased sensors and data storage. These datasets hold valuable patterns, like in neuromarketing, where researchers try to link patterns in multivariate sequential data from physiological sensors to the purchase behavior of products and services. But a lack of ground truth and high variance make automatic pattern detection unreliable. Our advancements are based on a novel query-aware locality-sensitive hashing technique to create a feature-based representation of multivariate time series windows. Most importantly, our algorithm features sub-linear training and inference time. We can even accomplish both the modeling and comparison of 10,000 different 64-track time series, each with 100 time steps (a typical EEG dataset) under 0.8 seconds. This performance gain allows for a rapid relevance feedback-driven adaption of the underlying pattern similarity model and enables the user to modify the speed-vs-accuracy trade-off gradually. We demonstrate superiority of PSEUDo in terms of efficiency, accuracy, and steerability through a quantitative performance comparison and a qualitative visual quality comparison to the state-of-the-art algorithms in the field. Moreover, we showcase the usability of PSEUDo through a case study demonstrating our visual pattern retrieval concepts in a large meteorological dataset. We find that our adaptive models can accurately capture the user's notion of similarity and allow for an understandable exploratory visual pattern retrieval in large multivariate time series datasets.

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We present PSEUDo, an adaptive feature learning technique for exploring visual patterns in multi-track sequential data. Our approach is designed with the primary focus to overcome the uneconomic retraining requirements and inflexible representation learning in current deep learning-based systems. Multi-track time series data are generated on an unprecedented scale due to increased sensors and data storage. These datasets hold valuable patterns, like in neuromarketing, where researchers try to link patterns in multivariate sequential data from physiological sensors to the purchase behavior of products and services. But a lack of ground truth and high variance make automatic pattern detection unreliable. Our advancements are based on a novel query-aware locality-sensitive hashing technique to create a feature-based representation of multivariate time series windows. Most importantly, our algorithm features sub-linear training and inference time. We can even accomplish both the modeling and comparison of 10,000 different 64-track time series, each with 100 time steps (a typical EEG dataset) under 0.8 seconds. This performance gain allows for a rapid relevance feedback-driven adaption of the underlying pattern similarity model and enables the user to modify the speed-vs-accuracy trade-off gradually. We demonstrate superiority of PSEUDo in terms of efficiency, accuracy, and steerability through a quantitative performance comparison and a qualitative visual quality comparison to the state-of-the-art algorithms in the field. Moreover, we showcase the usability of PSEUDo through a case study demonstrating our visual pattern retrieval concepts in a large meteorological dataset. We find that our adaptive models can accurately capture the user's notion of similarity and allow for an understandable exploratory visual pattern retrieval in large multivariate time series datasets.

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