Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing me...Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.展开更多
Sexism of language can be defined as prejudice or discrimination against people, especially women, showing in actual language use. This paper attempts to explore sexism in language system through examining various asy...Sexism of language can be defined as prejudice or discrimination against people, especially women, showing in actual language use. This paper attempts to explore sexism in language system through examining various asymmetric phenomena in language forms, word meanings and address forms.展开更多
In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language proc...In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.展开更多
Language is a tool for people to use to communicate, but the cultural, historical, economic and social reasons create gender discrimination in the English language. This article will analyze this kind of phenomenon, a...Language is a tool for people to use to communicate, but the cultural, historical, economic and social reasons create gender discrimination in the English language. This article will analyze this kind of phenomenon, and it will discuss the solution.展开更多
Language is the cartier of culture, and also it is a mirror of society. Different social and cultural gender discrimination are inevitably reflected through the use of language. This article studies gender discriminat...Language is the cartier of culture, and also it is a mirror of society. Different social and cultural gender discrimination are inevitably reflected through the use of language. This article studies gender discrimination in English and Chinese respectively, from the structre of words, title, name, word order, vocabulary and slang and other aspects.展开更多
基金National Natural Science Foundation of China(No.61971121)。
文摘Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.
文摘Sexism of language can be defined as prejudice or discrimination against people, especially women, showing in actual language use. This paper attempts to explore sexism in language system through examining various asymmetric phenomena in language forms, word meanings and address forms.
基金Project(60763001)supported by the National Natural Science Foundation of ChinaProjects(2009GZS0027,2010GZS0072)supported by the Natural Science Foundation of Jiangxi Province,China
文摘In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.
文摘Language is a tool for people to use to communicate, but the cultural, historical, economic and social reasons create gender discrimination in the English language. This article will analyze this kind of phenomenon, and it will discuss the solution.
文摘Language is the cartier of culture, and also it is a mirror of society. Different social and cultural gender discrimination are inevitably reflected through the use of language. This article studies gender discrimination in English and Chinese respectively, from the structre of words, title, name, word order, vocabulary and slang and other aspects.
