Label distribution learning for scene text detection

Recently,segmentation-based scene text detection has drawn a wide research interest due to its flexibility in describing scene text instance of arbitrary shapes such as curved texts.However,existing methods usually need complex post-processing stages to process ambiguous labels,i.e.,the labels of the pixels near the text boundary,which may belong to the text or background.In this paper,we present a framework for segmentation-based scene text detection by learning from ambiguous labels.We use the label distribution learning method to process the label ambiguity of text annotation,which achieves a good performance without using additional post-processing stage.Experiments on benchmark datasets demonstrate that our method produces better results than state-of-the-art methods for segmentation-based scene text detection.

Active label distribution learning via kernel maximum mean discrepancy

Label distribution learning(LDL)is a new learning paradigm to deal with label ambiguity and many researches have achieved the prominent performances.Compared with traditional supervised learning scenarios,the annotation with label distribution is more expensive.Direct use of existing active learning(AL)approaches,which aim to reduce the annotation cost in traditional learning,may lead to the degradation of their performance.To deal with the problem of high annotation cost in LDL,we propose the active label distribution learning via kernel maximum mean discrepancy(ALDL-kMMD)method to tackle this crucial but rarely studied problem.ALDL-kMMD captures the structural information of both data and label,extracts the most representative instances from the unlabeled ones by incorporating the nonlinear model and marginal probability distribution matching.Besides,it is also able to markedly decrease the amount of queried unlabeled instances.Meanwhile,an effective solution is proposed for the original optimization problem of ALDL-kMMD by constructing auxiliary variables.The effectiveness of our method is validated with experiments on the real-world datasets.

Practical age estimation using deep label distribution learning

Age estimation plays an important role in human-computer interaction system.The lack of large number of facial images with definite age label makes age estimation al-gorithms inefficient.Deep label distribution learning(DLDL)which employs convolutional neural networks(CNN)and label distribution learning to learn ambiguity from ground-truth age and adjacent ages,has been proven to outperform current state-of-the-art framework.However,DLDL assumes a rough label distribution which covers all ages for any given age label.In this paper,a more practical label distribution paradigm is proposed:we limit age label distribution that only covers a reasonable number of neighboring ages.In addition,we explore different label distributions to improve the performance of the proposed learning model.We employ CNN and the improved label distribution learning to estimate age.Experimental results show that compared to the DLDL,our method is more effective for facial age recognition.

Label distribution expression recognition algorithm based on asymptotic truth value

Ambiguous expression is a common phenomenon in facial expression recognition(FER).Because of the existence of ambiguous expression,the effect of FER is severely limited.The reason maybe that the single label of the data cannot effectively describe complex emotional intentions which are vital in FER.Label distribution learning contains more information and is a possible way to solve this problem.To apply label distribution learning on FER,a label distribution expression recognition algorithm based on asymptotic truth value is proposed.Under the premise of not incorporating extraneous quantitative information,the original information of database is fully used to complete the generation and utilization of label distribution.Firstly,in training part,single label learning is used to collect the mean value of the overall distribution of data.Then,the true value of data label is approached gradually on the granularity of data batch.Finally,the whole network model is retrained using the generated label distribution data.Experimental results show that this method can improve the accuracy of the network model obviously,and has certain competitiveness compared with the advanced algorithms.

Label distribution for multimodal machine learning

Multimodal machine learning(MML)aims to understand the world from multiple related modalities.It has attracted much attention as multimodal data has become increasingly available in real-world application.It is shown that MML can perform better than single-modal machine learning,since multi-modalities containing more information which could complement each other.However,it is a key challenge to fuse the multi-modalities in MML.Different from previous work,we further consider the side-information,which reflects the situation and influences the fusion of multi-modalities.We recover multimodal label distribution(MLD)by leveraging the side-information,representing the degree to which each modality contributes to describing the instance.Accordingly,a novel framework named multimodal label distribution learning(MLDL)is proposed to recover the MLD,and fuse the multimodalities with its guidance to learn an in-depth understanding of the jointly feature representation.Moreover,two versions of MLDL are proposed to deal with the sequential data.Experiments on multimodal sentiment analysis and disease prediction show that the proposed approaches perform favorably against state-of-the-art methods.

A knowledge-based approach for estimating the distribution of urban mixed land use

Estimating the proportion of land-use types in different regions is essential to promote the organization of a compact city and reduce energy consumption.However,existing research in this area has a few limitations:(1)lack of consideration of land-use distribution-related factors other than POIs;(2)inability to extract complex relations from heterogeneous information;and(3)overlooking the correlation between land-use types.To overcome these limitations,we propose a knowledge-based approach for estimating land-use distributions.We designed a knowledge graph to display POIs and other related heterogeneous data and then utilized a knowledge embedding model to directly obtain the region embedding vectors by learning the complex and implicit relations present in the knowledge graph.Region embedding vectors were mapped to land-use distributions using a label distribution learning method integrating the correlation between land-use types.To prove the reliability and validity of our approach,we conducted a case study in Jinhua,China.The results indicated that the proposed model outperformed other algorithms in all evaluation indices,thus illustrating the potential of this method to achieve higher accuracy land-use distribution estimates.